Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionAccuracyToCoveredSamples.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionAccuracyToCoveredSamples.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionAccuracyToCoveredSamples.cs	(revision 8811)
@@ -28,5 +28,4 @@
 using HeuristicLab.Data;
 using HeuristicLab.MainForm;
-using HeuristicLab.Problems.DataAnalysis.Interfaces;
 
 namespace HeuristicLab.Problems.DataAnalysis.Views {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionConfidenceAccuracyDependence.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionConfidenceAccuracyDependence.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionConfidenceAccuracyDependence.cs	(revision 8811)
@@ -27,5 +27,4 @@
 using HeuristicLab.Data;
 using HeuristicLab.MainForm;
-using HeuristicLab.Problems.DataAnalysis.Interfaces;
 
 namespace HeuristicLab.Problems.DataAnalysis.Views {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionEstimatedClassValuesView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionEstimatedClassValuesView.Designer.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionEstimatedClassValuesView.Designer.cs	(revision 8811)
@@ -46,7 +46,7 @@
       this.SamplesComboBox = new System.Windows.Forms.ComboBox();
       this.CorrectClassifiedConfidenceLabel = new System.Windows.Forms.Label();
-      this.CorrectClassifiedConfidence = new System.Windows.Forms.Label();
       this.WrongClassifiedConfidenceLabel = new System.Windows.Forms.Label();
-      this.WrongClassifiedConfidence = new System.Windows.Forms.Label();
+      this.CorrectClassifiedConfidence = new System.Windows.Forms.TextBox();
+      this.WrongClassifiedConfidence = new System.Windows.Forms.TextBox();
       this.SuspendLayout();
       // 
@@ -68,5 +68,5 @@
       this.SamplesComboBox.Location = new System.Drawing.Point(4, 4);
       this.SamplesComboBox.Name = "SamplesComboBox";
-      this.SamplesComboBox.Size = new System.Drawing.Size(309, 21);
+      this.SamplesComboBox.Size = new System.Drawing.Size(230, 21);
       this.SamplesComboBox.TabIndex = 2;
       this.SamplesComboBox.SelectedIndexChanged += new System.EventHandler(this.SamplesComboBox_SelectedIndexChanged);
@@ -76,5 +76,5 @@
       this.CorrectClassifiedConfidenceLabel.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Right)));
       this.CorrectClassifiedConfidenceLabel.AutoSize = true;
-      this.CorrectClassifiedConfidenceLabel.Location = new System.Drawing.Point(319, 7);
+      this.CorrectClassifiedConfidenceLabel.Location = new System.Drawing.Point(240, 7);
       this.CorrectClassifiedConfidenceLabel.Name = "CorrectClassifiedConfidenceLabel";
       this.CorrectClassifiedConfidenceLabel.Size = new System.Drawing.Size(103, 13);
@@ -82,19 +82,9 @@
       this.CorrectClassifiedConfidenceLabel.Text = "Conf. Correct Class.:";
       // 
-      // CorrectClassifiedConfidence
-      // 
-      this.CorrectClassifiedConfidence.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Right)));
-      this.CorrectClassifiedConfidence.AutoSize = true;
-      this.CorrectClassifiedConfidence.Location = new System.Drawing.Point(428, 7);
-      this.CorrectClassifiedConfidence.Name = "CorrectClassifiedConfidence";
-      this.CorrectClassifiedConfidence.Size = new System.Drawing.Size(22, 13);
-      this.CorrectClassifiedConfidence.TabIndex = 4;
-      this.CorrectClassifiedConfidence.Text = "0.0";
-      // 
       // WrongClassifiedConfidenceLabel
       // 
       this.WrongClassifiedConfidenceLabel.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Right)));
       this.WrongClassifiedConfidenceLabel.AutoSize = true;
-      this.WrongClassifiedConfidenceLabel.Location = new System.Drawing.Point(481, 7);
+      this.WrongClassifiedConfidenceLabel.Location = new System.Drawing.Point(455, 7);
       this.WrongClassifiedConfidenceLabel.Name = "WrongClassifiedConfidenceLabel";
       this.WrongClassifiedConfidenceLabel.Size = new System.Drawing.Size(101, 13);
@@ -102,21 +92,30 @@
       this.WrongClassifiedConfidenceLabel.Text = "Conf. Wrong Class.:";
       // 
+      // CorrectClassifiedConfidence
+      // 
+      this.CorrectClassifiedConfidence.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Right)));
+      this.CorrectClassifiedConfidence.Location = new System.Drawing.Point(349, 5);
+      this.CorrectClassifiedConfidence.Name = "CorrectClassifiedConfidence";
+      this.CorrectClassifiedConfidence.ReadOnly = true;
+      this.CorrectClassifiedConfidence.Size = new System.Drawing.Size(100, 20);
+      this.CorrectClassifiedConfidence.TabIndex = 7;
+      this.CorrectClassifiedConfidence.Text = "0.0";
+      // 
       // WrongClassifiedConfidence
       // 
       this.WrongClassifiedConfidence.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Right)));
-      this.WrongClassifiedConfidence.AutoSize = true;
-      this.WrongClassifiedConfidence.Location = new System.Drawing.Point(588, 7);
+      this.WrongClassifiedConfidence.Location = new System.Drawing.Point(562, 4);
       this.WrongClassifiedConfidence.Name = "WrongClassifiedConfidence";
-      this.WrongClassifiedConfidence.Size = new System.Drawing.Size(22, 13);
-      this.WrongClassifiedConfidence.TabIndex = 6;
+      this.WrongClassifiedConfidence.ReadOnly = true;
+      this.WrongClassifiedConfidence.Size = new System.Drawing.Size(94, 20);
+      this.WrongClassifiedConfidence.TabIndex = 8;
       this.WrongClassifiedConfidence.Text = "0.0";
       // 
       // ClassificationEnsembleSolutionEstimatedClassValuesView
       // 
-      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
       this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Inherit;
       this.Controls.Add(this.WrongClassifiedConfidence);
+      this.Controls.Add(this.CorrectClassifiedConfidence);
       this.Controls.Add(this.WrongClassifiedConfidenceLabel);
-      this.Controls.Add(this.CorrectClassifiedConfidence);
       this.Controls.Add(this.CorrectClassifiedConfidenceLabel);
       this.Controls.Add(this.SamplesComboBox);
@@ -126,6 +125,6 @@
       this.Controls.SetChildIndex(this.SamplesComboBox, 0);
       this.Controls.SetChildIndex(this.CorrectClassifiedConfidenceLabel, 0);
+      this.Controls.SetChildIndex(this.WrongClassifiedConfidenceLabel, 0);
       this.Controls.SetChildIndex(this.CorrectClassifiedConfidence, 0);
-      this.Controls.SetChildIndex(this.WrongClassifiedConfidenceLabel, 0);
       this.Controls.SetChildIndex(this.WrongClassifiedConfidence, 0);
       this.ResumeLayout(false);
@@ -138,7 +137,7 @@
     private System.Windows.Forms.ComboBox SamplesComboBox;
     private System.Windows.Forms.Label CorrectClassifiedConfidenceLabel;
-    private System.Windows.Forms.Label CorrectClassifiedConfidence;
     private System.Windows.Forms.Label WrongClassifiedConfidenceLabel;
-    private System.Windows.Forms.Label WrongClassifiedConfidence;
+    private System.Windows.Forms.TextBox CorrectClassifiedConfidence;
+    private System.Windows.Forms.TextBox WrongClassifiedConfidence;
 
   }
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionEstimatedClassValuesView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionEstimatedClassValuesView.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationEnsembleSolutionEstimatedClassValuesView.cs	(revision 8811)
@@ -19,4 +19,5 @@
  */
 #endregion
+
 using System;
 using System.Collections.Generic;
@@ -28,5 +29,4 @@
 using HeuristicLab.MainForm;
 using HeuristicLab.MainForm.WindowsForms;
-using HeuristicLab.Problems.DataAnalysis.Interfaces;
 
 namespace HeuristicLab.Problems.DataAnalysis.Views {
@@ -96,5 +96,5 @@
 
       IEnumerable<IClassificationSolution> solutions = Content.ClassificationSolutions.CheckedItems;
-      int classValuesCount = Content.ProblemData.ClassValues.Count;
+      int classValuesCount = Content.ProblemData.Classes;
       int solutionsCount = solutions.Count();
       string[,] values = new string[indizes.Length, 5 + classValuesCount + solutionsCount];
@@ -143,6 +143,7 @@
           var groups =
             estimatedValuesVector[i].GroupBy(x => x).Select(g => new { Key = g.Key, Count = g.Count() }).ToList();
-          for (int classIndex = 0; classIndex < Content.ProblemData.ClassValues.Count; classIndex++) {
-            var group = groups.Where(g => g.Key == Content.ProblemData.ClassValues[classIndex]).SingleOrDefault();
+
+          for (int classIndex = 0; classIndex < Content.ProblemData.Classes; classIndex++) {
+            var group = groups.Where(g => g.Key == Content.ProblemData.ClassValues.ElementAt(classIndex)).SingleOrDefault();
             if (group == null) values[i, 5 + classIndex] = 0.ToString();
             else values[i, 5 + classIndex] = group.Count.ToString();
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationFeatureCorrelationView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationFeatureCorrelationView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationFeatureCorrelationView.Designer.cs	(revision 8811)
@@ -0,0 +1,32 @@
+﻿namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class ClassificationFeatureCorrelationView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      components = new System.ComponentModel.Container();
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+    }
+
+    #endregion
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationFeatureCorrelationView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationFeatureCorrelationView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationFeatureCorrelationView.cs	(revision 8811)
@@ -0,0 +1,46 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Linq;
+using HeuristicLab.MainForm;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Classification Feature Correlation View")]
+  [Content(typeof(ClassificationProblemData), false)]
+  public partial class ClassificationFeatureCorrelationView : FeatureCorrelationView {
+
+    public new ClassificationProblemData Content {
+      get { return (ClassificationProblemData)base.Content; }
+      set { base.Content = value; }
+    }
+
+    public ClassificationFeatureCorrelationView() {
+      InitializeComponent();
+    }
+
+    protected override bool[] SetInitialVisibilityOfColumns() {
+      int i = Content.Dataset.DoubleVariables.ToList().FindIndex(x => x == Content.TargetVariable);
+      var initialVisibility = base.SetInitialVisibilityOfColumns();
+      initialVisibility[i] = true;
+      return initialVisibility;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationTimeframeFeatureCorrelationView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationTimeframeFeatureCorrelationView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationTimeframeFeatureCorrelationView.Designer.cs	(revision 8811)
@@ -0,0 +1,32 @@
+﻿namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class ClassificationTimeframeFeatureCorrelationView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      components = new System.ComponentModel.Container();
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+    }
+
+    #endregion
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationTimeframeFeatureCorrelationView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationTimeframeFeatureCorrelationView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/ClassificationTimeframeFeatureCorrelationView.cs	(revision 8811)
@@ -0,0 +1,46 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Linq;
+using HeuristicLab.MainForm;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Classification Timeframe Feature Correlation View")]
+  [Content(typeof(ClassificationProblemData), false)]
+  public partial class ClassificationTimeframeFeatureCorrelationView : TimeframeFeatureCorrelationView {
+
+    public new ClassificationProblemData Content {
+      get { return (ClassificationProblemData)base.Content; }
+      set { base.Content = value; }
+    }
+
+    public ClassificationTimeframeFeatureCorrelationView() {
+      InitializeComponent();
+    }
+
+    protected override bool[] SetInitialVisibilityOfColumns() {
+      int i = Content.Dataset.DoubleVariables.ToList().FindIndex(x => x == Content.TargetVariable);
+      var initialVisibility = base.SetInitialVisibilityOfColumns();
+      initialVisibility[i] = true;
+      return initialVisibility;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationModelView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationModelView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationModelView.Designer.cs	(revision 8811)
@@ -0,0 +1,59 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class DiscriminantFunctionClassificationModelView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.SuspendLayout();
+
+      // 
+      // RegressionSolutionEnsembleModelView
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Inherit;
+      this.Name = "DiscriminantFunctionClassificationModelView";
+      this.ResumeLayout(false);
+    }
+
+    #endregion
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationModelView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationModelView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationModelView.cs	(revision 8811)
@@ -0,0 +1,52 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Windows.Forms;
+using HeuristicLab.MainForm;
+using HeuristicLab.MainForm.WindowsForms;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Discriminant Function Classification Model")]
+  [Content(typeof(DiscriminantFunctionClassificationModel), true)]
+  internal sealed partial class DiscriminantFunctionClassificationModelView : AsynchronousContentView {
+    private ViewHost view;
+
+    public DiscriminantFunctionClassificationModelView() {
+      InitializeComponent();
+      view = new ViewHost();
+      view.Dock = DockStyle.Fill;
+      Controls.Add(view);
+    }
+
+    public new DiscriminantFunctionClassificationModel Content {
+      get { return (DiscriminantFunctionClassificationModel)base.Content; }
+      set { base.Content = value; }
+    }
+
+    protected override void OnContentChanged() {
+      base.OnContentChanged();
+      if (Content != null) {
+        view.Content = Content.Model;
+      } else
+        view.Content = null;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationRocCurvesView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationRocCurvesView.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationRocCurvesView.cs	(revision 8811)
@@ -114,5 +114,5 @@
         maxThreshold += thresholdIncrement;
 
-        List<double> classValues = Content.ProblemData.ClassValues.OrderBy(x => x).ToList();
+        List<double> classValues = Content.Model.ClassValues.ToList();
 
         foreach (double classValue in classValues) {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationSolutionThresholdView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationSolutionThresholdView.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Classification/DiscriminantFunctionClassificationSolutionThresholdView.cs	(revision 8811)
@@ -238,4 +238,5 @@
       double[] thresholds = Content.Model.Thresholds.ToArray();
       thresholds[classIndex] = e.NewLocationY;
+      Array.Sort(thresholds);
       Content.Model.SetThresholdsAndClassValues(thresholds, Content.Model.ClassValues);
     }
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.Designer.cs	(revision 8811)
@@ -0,0 +1,275 @@
+﻿#region License Information
+
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#endregion
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class AbstractFeatureCorrelationView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.components = new System.ComponentModel.Container();
+      System.ComponentModel.ComponentResourceManager resources = new System.ComponentModel.ComponentResourceManager(typeof(AbstractFeatureCorrelationView));
+      this.DataGridView = new System.Windows.Forms.DataGridView();
+      this.HeatMapProgressBar = new System.Windows.Forms.ProgressBar();
+      this.PartitionComboBox = new System.Windows.Forms.ComboBox();
+      this.CorrelationCalcLabel = new System.Windows.Forms.Label();
+      this.CorrelationCalcComboBox = new System.Windows.Forms.ComboBox();
+      this.PartitionLabel = new System.Windows.Forms.Label();
+      this.minimumLabel = new System.Windows.Forms.Label();
+      this.maximumLabel = new System.Windows.Forms.Label();
+      this.PictureBox = new System.Windows.Forms.PictureBox();
+      this.SplitContainer = new System.Windows.Forms.SplitContainer();
+      this.CalculatingPanel = new System.Windows.Forms.Panel();
+      this.CalculatingLabel = new System.Windows.Forms.Label();
+      this.contextMenu = new System.Windows.Forms.ContextMenuStrip(this.components);
+      this.ShowHideColumns = new System.Windows.Forms.ToolStripMenuItem();
+      ((System.ComponentModel.ISupportInitialize)(this.DataGridView)).BeginInit();
+      ((System.ComponentModel.ISupportInitialize)(this.PictureBox)).BeginInit();
+      ((System.ComponentModel.ISupportInitialize)(this.SplitContainer)).BeginInit();
+      this.SplitContainer.Panel1.SuspendLayout();
+      this.SplitContainer.Panel2.SuspendLayout();
+      this.SplitContainer.SuspendLayout();
+      this.CalculatingPanel.SuspendLayout();
+      this.contextMenu.SuspendLayout();
+      this.SuspendLayout();
+      // 
+      // DataGridView
+      // 
+      this.DataGridView.AllowUserToAddRows = false;
+      this.DataGridView.AllowUserToDeleteRows = false;
+      this.DataGridView.ColumnHeadersHeightSizeMode = System.Windows.Forms.DataGridViewColumnHeadersHeightSizeMode.AutoSize;
+      this.DataGridView.Dock = System.Windows.Forms.DockStyle.Fill;
+      this.DataGridView.Location = new System.Drawing.Point(0, 0);
+      this.DataGridView.Name = "DataGridView";
+      this.DataGridView.ReadOnly = true;
+      this.DataGridView.Size = new System.Drawing.Size(475, 301);
+      this.DataGridView.TabIndex = 0;
+      this.DataGridView.VirtualMode = true;
+      this.DataGridView.CellPainting += new System.Windows.Forms.DataGridViewCellPaintingEventHandler(this.DataGridView_CellPainting);
+      this.DataGridView.CellValueNeeded += new System.Windows.Forms.DataGridViewCellValueEventHandler(this.DataGridView_CellValueNeeded);
+      this.DataGridView.ColumnHeaderMouseClick += new System.Windows.Forms.DataGridViewCellMouseEventHandler(this.DataGridView_ColumnHeaderMouseClick);
+      this.DataGridView.KeyDown += new System.Windows.Forms.KeyEventHandler(this.DataGridView_KeyDown);
+      this.DataGridView.MouseClick += new System.Windows.Forms.MouseEventHandler(this.DataGridView_MouseClick);
+      // 
+      // HeatMapProgressBar
+      // 
+      this.HeatMapProgressBar.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Left | System.Windows.Forms.AnchorStyles.Right)));
+      this.HeatMapProgressBar.Location = new System.Drawing.Point(25, 46);
+      this.HeatMapProgressBar.Name = "HeatMapProgressBar";
+      this.HeatMapProgressBar.RightToLeft = System.Windows.Forms.RightToLeft.No;
+      this.HeatMapProgressBar.Size = new System.Drawing.Size(154, 21);
+      this.HeatMapProgressBar.TabIndex = 9;
+      // 
+      // PartitionComboBox
+      // 
+      this.PartitionComboBox.DropDownStyle = System.Windows.Forms.ComboBoxStyle.DropDownList;
+      this.PartitionComboBox.FormattingEnabled = true;
+      this.PartitionComboBox.Location = new System.Drawing.Point(333, 3);
+      this.PartitionComboBox.Name = "PartitionComboBox";
+      this.PartitionComboBox.Size = new System.Drawing.Size(142, 21);
+      this.PartitionComboBox.TabIndex = 8;
+      this.PartitionComboBox.SelectionChangeCommitted += new System.EventHandler(this.PartitionComboBox_SelectedChangeCommitted);
+      // 
+      // CorrelationCalcLabel
+      // 
+      this.CorrelationCalcLabel.AutoSize = true;
+      this.CorrelationCalcLabel.Location = new System.Drawing.Point(0, 6);
+      this.CorrelationCalcLabel.Name = "CorrelationCalcLabel";
+      this.CorrelationCalcLabel.Size = new System.Drawing.Size(104, 13);
+      this.CorrelationCalcLabel.TabIndex = 7;
+      this.CorrelationCalcLabel.Text = "Correlation Measure:";
+      // 
+      // CorrelationCalcComboBox
+      // 
+      this.CorrelationCalcComboBox.DropDownStyle = System.Windows.Forms.ComboBoxStyle.DropDownList;
+      this.CorrelationCalcComboBox.FormattingEnabled = true;
+      this.CorrelationCalcComboBox.Location = new System.Drawing.Point(110, 3);
+      this.CorrelationCalcComboBox.Name = "CorrelationCalcComboBox";
+      this.CorrelationCalcComboBox.Size = new System.Drawing.Size(163, 21);
+      this.CorrelationCalcComboBox.TabIndex = 6;
+      this.CorrelationCalcComboBox.SelectionChangeCommitted += new System.EventHandler(this.CorrelationMeasureComboBox_SelectedChangeCommitted);
+      // 
+      // PartitionLabel
+      // 
+      this.PartitionLabel.AutoSize = true;
+      this.PartitionLabel.Location = new System.Drawing.Point(279, 6);
+      this.PartitionLabel.Name = "PartitionLabel";
+      this.PartitionLabel.Size = new System.Drawing.Size(48, 13);
+      this.PartitionLabel.TabIndex = 10;
+      this.PartitionLabel.Text = "Partition:";
+      // 
+      // minimumLabel
+      // 
+      this.minimumLabel.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Bottom | System.Windows.Forms.AnchorStyles.Right)));
+      this.minimumLabel.BackColor = System.Drawing.Color.Transparent;
+      this.minimumLabel.Location = new System.Drawing.Point(487, 314);
+      this.minimumLabel.Name = "minimumLabel";
+      this.minimumLabel.Size = new System.Drawing.Size(73, 19);
+      this.minimumLabel.TabIndex = 13;
+      this.minimumLabel.Text = "0.0";
+      this.minimumLabel.TextAlign = System.Drawing.ContentAlignment.TopCenter;
+      // 
+      // maximumLabel
+      // 
+      this.maximumLabel.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Right)));
+      this.maximumLabel.BackColor = System.Drawing.Color.Transparent;
+      this.maximumLabel.Location = new System.Drawing.Point(487, 2);
+      this.maximumLabel.Name = "maximumLabel";
+      this.maximumLabel.Size = new System.Drawing.Size(73, 25);
+      this.maximumLabel.TabIndex = 12;
+      this.maximumLabel.Text = "1.0";
+      this.maximumLabel.TextAlign = System.Drawing.ContentAlignment.BottomCenter;
+      // 
+      // PictureBox
+      // 
+      this.PictureBox.Anchor = ((System.Windows.Forms.AnchorStyles)(((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Bottom)
+                  | System.Windows.Forms.AnchorStyles.Right)));
+      this.PictureBox.BorderStyle = System.Windows.Forms.BorderStyle.FixedSingle;
+      this.PictureBox.Image = ((System.Drawing.Image)(resources.GetObject("PictureBox.Image")));
+      this.PictureBox.Location = new System.Drawing.Point(507, 30);
+      this.PictureBox.Name = "PictureBox";
+      this.PictureBox.Size = new System.Drawing.Size(35, 281);
+      this.PictureBox.SizeMode = System.Windows.Forms.PictureBoxSizeMode.StretchImage;
+      this.PictureBox.TabIndex = 15;
+      this.PictureBox.TabStop = false;
+      // 
+      // SplitContainer
+      // 
+      this.SplitContainer.Anchor = ((System.Windows.Forms.AnchorStyles)((((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Bottom)
+                  | System.Windows.Forms.AnchorStyles.Left)
+                  | System.Windows.Forms.AnchorStyles.Right)));
+      this.SplitContainer.FixedPanel = System.Windows.Forms.FixedPanel.Panel1;
+      this.SplitContainer.IsSplitterFixed = true;
+      this.SplitContainer.Location = new System.Drawing.Point(3, 3);
+      this.SplitContainer.Name = "SplitContainer";
+      this.SplitContainer.Orientation = System.Windows.Forms.Orientation.Horizontal;
+      // 
+      // SplitContainer.Panel1
+      // 
+      this.SplitContainer.Panel1.Controls.Add(this.CorrelationCalcLabel);
+      this.SplitContainer.Panel1.Controls.Add(this.CorrelationCalcComboBox);
+      this.SplitContainer.Panel1.Controls.Add(this.PartitionComboBox);
+      this.SplitContainer.Panel1.Controls.Add(this.PartitionLabel);
+      // 
+      // SplitContainer.Panel2
+      // 
+      this.SplitContainer.Panel2.Controls.Add(this.CalculatingPanel);
+      this.SplitContainer.Panel2.Controls.Add(this.DataGridView);
+      this.SplitContainer.Size = new System.Drawing.Size(475, 330);
+      this.SplitContainer.SplitterDistance = 25;
+      this.SplitContainer.TabIndex = 16;
+      // 
+      // CalculatingPanel
+      // 
+      this.CalculatingPanel.Anchor = System.Windows.Forms.AnchorStyles.None;
+      this.CalculatingPanel.Controls.Add(this.CalculatingLabel);
+      this.CalculatingPanel.Controls.Add(this.HeatMapProgressBar);
+      this.CalculatingPanel.Location = new System.Drawing.Point(138, 95);
+      this.CalculatingPanel.Name = "CalculatingPanel";
+      this.CalculatingPanel.Size = new System.Drawing.Size(200, 81);
+      this.CalculatingPanel.TabIndex = 10;
+      // 
+      // CalculatingLabel
+      // 
+      this.CalculatingLabel.AutoSize = true;
+      this.CalculatingLabel.Location = new System.Drawing.Point(42, 19);
+      this.CalculatingLabel.Name = "CalculatingLabel";
+      this.CalculatingLabel.Size = new System.Drawing.Size(120, 13);
+      this.CalculatingLabel.TabIndex = 10;
+      this.CalculatingLabel.Text = "Calculating correlation...";
+      // 
+      // contextMenu
+      // 
+      this.contextMenu.Items.AddRange(new System.Windows.Forms.ToolStripItem[] {
+            this.ShowHideColumns});
+      this.contextMenu.Name = "contextMenu";
+      this.contextMenu.Size = new System.Drawing.Size(191, 26);
+      // 
+      // ShowHideColumns
+      // 
+      this.ShowHideColumns.Name = "ShowHideColumns";
+      this.ShowHideColumns.Size = new System.Drawing.Size(190, 22);
+      this.ShowHideColumns.Text = "Show / Hide Columns";
+      this.ShowHideColumns.Click += new System.EventHandler(this.ShowHideColumns_Click);
+      // 
+      // AbstractFeatureCorrelationView
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+      this.Controls.Add(this.SplitContainer);
+      this.Controls.Add(this.minimumLabel);
+      this.Controls.Add(this.PictureBox);
+      this.Controls.Add(this.maximumLabel);
+      this.Name = "AbstractFeatureCorrelationView";
+      this.Size = new System.Drawing.Size(569, 336);
+      ((System.ComponentModel.ISupportInitialize)(this.DataGridView)).EndInit();
+      ((System.ComponentModel.ISupportInitialize)(this.PictureBox)).EndInit();
+      this.SplitContainer.Panel1.ResumeLayout(false);
+      this.SplitContainer.Panel1.PerformLayout();
+      this.SplitContainer.Panel2.ResumeLayout(false);
+      ((System.ComponentModel.ISupportInitialize)(this.SplitContainer)).EndInit();
+      this.SplitContainer.ResumeLayout(false);
+      this.CalculatingPanel.ResumeLayout(false);
+      this.CalculatingPanel.PerformLayout();
+      this.contextMenu.ResumeLayout(false);
+      this.ResumeLayout(false);
+
+    }
+
+    #endregion
+
+    protected System.Windows.Forms.DataGridView DataGridView;
+    protected System.Windows.Forms.ProgressBar HeatMapProgressBar;
+    protected System.Windows.Forms.ComboBox PartitionComboBox;
+    protected System.Windows.Forms.Label CorrelationCalcLabel;
+    protected System.Windows.Forms.ComboBox CorrelationCalcComboBox;
+    protected System.Windows.Forms.Label PartitionLabel;
+    protected System.Windows.Forms.Label minimumLabel;
+    protected System.Windows.Forms.Label maximumLabel;
+    protected System.Windows.Forms.PictureBox PictureBox;
+    protected System.Windows.Forms.SplitContainer SplitContainer;
+    protected System.Windows.Forms.Panel CalculatingPanel;
+    protected System.Windows.Forms.Label CalculatingLabel;
+    protected System.Windows.Forms.ContextMenuStrip contextMenu;
+    protected System.Windows.Forms.ToolStripMenuItem ShowHideColumns;
+
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.cs	(revision 8811)
@@ -0,0 +1,348 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Drawing;
+using System.Linq;
+using System.Text;
+using System.Windows.Forms;
+using HeuristicLab.Analysis;
+using HeuristicLab.Common;
+using HeuristicLab.Data.Views;
+using HeuristicLab.DataAnalysis.Views;
+using HeuristicLab.MainForm;
+using HeuristicLab.MainForm.WindowsForms;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Feature Correlation View")]
+  [Content(typeof(DataAnalysisProblemData), false)]
+  public abstract partial class AbstractFeatureCorrelationView : AsynchronousContentView {
+
+    private int[] virtualRowIndices;
+    private VariableVisibilityDialog variableVisibility;
+    private List<KeyValuePair<int, SortOrder>> sortedColumnIndices;
+    private StringConvertibleMatrixView.RowComparer rowComparer;
+
+    protected FeatureCorrelationCalculator fcc;
+    protected HeatMap currentCorrelation;
+
+    public new DataAnalysisProblemData Content {
+      get { return (DataAnalysisProblemData)base.Content; }
+      set { base.Content = value; }
+    }
+
+    protected AbstractFeatureCorrelationView() {
+      InitializeComponent();
+      sortedColumnIndices = new List<KeyValuePair<int, SortOrder>>();
+      rowComparer = new StringConvertibleMatrixView.RowComparer();
+      fcc = new FeatureCorrelationCalculator();
+      var calculatorList = FeatureCorrelationEnums.EnumToList<FeatureCorrelationEnums.CorrelationCalculators>().Select(x => new KeyValuePair<FeatureCorrelationEnums.CorrelationCalculators, string>(x, FeatureCorrelationEnums.GetEnumDescription(x))).ToList();
+      CorrelationCalcComboBox.ValueMember = "Key";
+      CorrelationCalcComboBox.DisplayMember = "Value";
+      CorrelationCalcComboBox.DataSource = new BindingList<KeyValuePair<FeatureCorrelationEnums.CorrelationCalculators, string>>(calculatorList);
+      var partitionList = FeatureCorrelationEnums.EnumToList<FeatureCorrelationEnums.Partitions>().Select(x => new KeyValuePair<FeatureCorrelationEnums.Partitions, string>(x, FeatureCorrelationEnums.GetEnumDescription(x))).ToList();
+      PartitionComboBox.ValueMember = "Key";
+      PartitionComboBox.DisplayMember = "Value";
+      PartitionComboBox.DataSource = new BindingList<KeyValuePair<FeatureCorrelationEnums.Partitions, string>>(partitionList);
+    }
+
+    protected override void RegisterContentEvents() {
+      base.RegisterContentEvents();
+      fcc.ProgressCalculation += new FeatureCorrelationCalculator.ProgressCalculationHandler(Content_ProgressCalculation);
+      fcc.CorrelationCalculationFinished += new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished);
+    }
+
+    protected override void DeregisterContentEvents() {
+      fcc.CorrelationCalculationFinished += new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished);
+      fcc.ProgressCalculation += new FeatureCorrelationCalculator.ProgressCalculationHandler(Content_ProgressCalculation);
+      base.DeregisterContentEvents();
+    }
+
+    protected override void OnContentChanged() {
+      base.OnContentChanged();
+      if (Content != null) {
+        fcc.ProblemData = Content;
+        bool[] initialVisibility = SetInitialVisibilityOfColumns();
+
+        variableVisibility = new VariableVisibilityDialog(Content.Dataset.DoubleVariables, initialVisibility);
+        variableVisibility.VariableVisibilityChanged += new ItemCheckEventHandler(variableVisibility_VariableVisibilityChanged);
+        CalculateCorrelation();
+      } else {
+        DataGridView.Columns.Clear();
+        DataGridView.Rows.Clear();
+      }
+    }
+
+    protected virtual bool[] SetInitialVisibilityOfColumns() {
+      bool[] initialVisibility = new bool[Content.Dataset.DoubleVariables.Count()];
+      int i = 0;
+      foreach (var variable in Content.Dataset.DoubleVariables) {
+        initialVisibility[i] = Content.AllowedInputVariables.Contains(variable);
+        i++;
+      }
+      return initialVisibility;
+    }
+
+    protected abstract void variableVisibility_VariableVisibilityChanged(object sender, ItemCheckEventArgs e);
+
+    protected void CorrelationMeasureComboBox_SelectedChangeCommitted(object sender, System.EventArgs e) {
+      CalculateCorrelation();
+    }
+    protected void PartitionComboBox_SelectedChangeCommitted(object sender, System.EventArgs e) {
+      CalculateCorrelation();
+    }
+
+    protected abstract void CalculateCorrelation();
+    protected abstract void Content_CorrelationCalculationFinished(object sender, FeatureCorrelationCalculator.CorrelationCalculationFinishedArgs e);
+
+    protected void UpdateDataGrid() {
+      virtualRowIndices = Enumerable.Range(0, currentCorrelation.Rows).ToArray();
+      DataGridViewColumn[] columns = new DataGridViewColumn[currentCorrelation.Columns];
+      for (int i = 0; i < columns.Length; ++i) {
+        var column = new DataGridViewTextBoxColumn();
+        column.FillWeight = 1;
+        columns[i] = column;
+      }
+
+      DataGridView.Columns.Clear();
+      DataGridView.Columns.AddRange(columns);
+
+      DataGridView.RowCount = currentCorrelation.Rows;
+
+      ClearSorting();
+      UpdateColumnHeaders();
+      UpdateRowHeaders();
+
+      maximumLabel.Text = currentCorrelation.Maximum.ToString();
+      minimumLabel.Text = currentCorrelation.Minimum.ToString();
+
+      DataGridView.AutoResizeColumns(DataGridViewAutoSizeColumnsMode.ColumnHeader);
+      DataGridView.AutoResizeRowHeadersWidth(DataGridViewRowHeadersWidthSizeMode.AutoSizeToDisplayedHeaders);
+      DataGridView.Enabled = true;
+    }
+
+    protected virtual void UpdateColumnHeaders() {
+      for (int i = 0; i < DataGridView.ColumnCount; i++) {
+        DataGridView.Columns[i].HeaderText = currentCorrelation.ColumnNames.ElementAt(i);
+        DataGridView.Columns[i].Visible = variableVisibility.Visibility[i];
+      }
+    }
+    protected virtual void UpdateRowHeaders() {
+      for (int i = 0; i < DataGridView.RowCount; i++) {
+        DataGridView.Rows[i].HeaderCell.Value = currentCorrelation.RowNames.ElementAt(virtualRowIndices[i]);
+        DataGridView.Rows[i].Visible = variableVisibility.Visibility[virtualRowIndices[i]];
+      }
+    }
+
+    protected void Content_ProgressCalculation(object sender, ProgressChangedEventArgs e) {
+      if (!CalculatingPanel.Visible && e.ProgressPercentage != HeatMapProgressBar.Maximum) {
+        CalculatingPanel.Show();
+      } else if (e.ProgressPercentage == HeatMapProgressBar.Maximum) {
+        CalculatingPanel.Hide();
+      }
+      HeatMapProgressBar.Value = e.ProgressPercentage;
+    }
+
+    protected void DataGridView_CellValueNeeded(object sender, DataGridViewCellValueEventArgs e) {
+      if (Content == null) return;
+      int rowIndex = virtualRowIndices[e.RowIndex];
+      e.Value = currentCorrelation[rowIndex, e.ColumnIndex];
+    }
+
+    protected void DataGridView_CellPainting(object sender, DataGridViewCellPaintingEventArgs e) {
+      if (Content == null) return;
+      if (e.RowIndex < 0) return;
+      if (e.ColumnIndex < 0) return;
+      if (e.State.HasFlag(DataGridViewElementStates.Selected)) return;
+      if (!e.PaintParts.HasFlag(DataGridViewPaintParts.Background)) return;
+
+      int rowIndex = virtualRowIndices[e.RowIndex];
+      Color backColor = GetDataPointColor(currentCorrelation[rowIndex, e.ColumnIndex], currentCorrelation.Minimum, currentCorrelation.Maximum);
+      using (Brush backColorBrush = new SolidBrush(backColor)) {
+        e.Graphics.FillRectangle(backColorBrush, e.CellBounds);
+      }
+      e.PaintContent(e.CellBounds);
+      e.Handled = true;
+    }
+
+    protected virtual Color GetDataPointColor(double value, double min, double max) {
+      if (double.IsNaN(value)) {
+        return Color.DarkGray;
+      }
+      IList<Color> colors = ColorGradient.Colors;
+      int index = (int)((colors.Count - 1) * (value - min) / (max - min));
+      if (index >= colors.Count) index = colors.Count - 1;
+      if (index < 0) index = 0;
+      return colors[index];
+    }
+
+    #region sort
+    protected void DataGridView_ColumnHeaderMouseClick(object sender, DataGridViewCellMouseEventArgs e) {
+      if (Content != null) {
+        if (e.Button == MouseButtons.Left) {
+          bool addToSortedIndices = (Control.ModifierKeys & Keys.Control) == Keys.Control;
+          SortOrder newSortOrder = SortOrder.Ascending;
+          if (sortedColumnIndices.Any(x => x.Key == e.ColumnIndex)) {
+            SortOrder oldSortOrder = sortedColumnIndices.Where(x => x.Key == e.ColumnIndex).First().Value;
+            int enumLength = Enum.GetValues(typeof(SortOrder)).Length;
+            newSortOrder = oldSortOrder = (SortOrder)Enum.Parse(typeof(SortOrder), ((((int)oldSortOrder) + 1) % enumLength).ToString());
+          }
+
+          if (!addToSortedIndices)
+            sortedColumnIndices.Clear();
+
+          if (sortedColumnIndices.Any(x => x.Key == e.ColumnIndex)) {
+            int sortedIndex = sortedColumnIndices.FindIndex(x => x.Key == e.ColumnIndex);
+            if (newSortOrder != SortOrder.None)
+              sortedColumnIndices[sortedIndex] = new KeyValuePair<int, SortOrder>(e.ColumnIndex, newSortOrder);
+            else
+              sortedColumnIndices.RemoveAt(sortedIndex);
+          } else
+            if (newSortOrder != SortOrder.None)
+              sortedColumnIndices.Add(new KeyValuePair<int, SortOrder>(e.ColumnIndex, newSortOrder));
+          Sort();
+        }
+      }
+    }
+
+    protected virtual void ClearSorting() {
+      virtualRowIndices = Enumerable.Range(0, currentCorrelation.Rows).ToArray();
+      sortedColumnIndices.Clear();
+      UpdateSortGlyph();
+    }
+
+    private void Sort() {
+      virtualRowIndices = Sort(sortedColumnIndices);
+      UpdateSortGlyph();
+      UpdateRowHeaders();
+      DataGridView.Invalidate();
+    }
+
+    protected virtual int[] Sort(IEnumerable<KeyValuePair<int, SortOrder>> sortedColumns) {
+      int[] newSortedIndex = Enumerable.Range(0, currentCorrelation.Rows).ToArray();
+      if (sortedColumns.Count() != 0) {
+        rowComparer.SortedIndices = sortedColumns;
+        rowComparer.Matrix = currentCorrelation;
+        Array.Sort(newSortedIndex, rowComparer);
+      }
+      return newSortedIndex;
+    }
+    private void UpdateSortGlyph() {
+      foreach (DataGridViewColumn col in this.DataGridView.Columns)
+        col.HeaderCell.SortGlyphDirection = SortOrder.None;
+      foreach (KeyValuePair<int, SortOrder> p in sortedColumnIndices)
+        this.DataGridView.Columns[p.Key].HeaderCell.SortGlyphDirection = p.Value;
+    }
+    #endregion
+
+    #region copy
+    protected void DataGridView_KeyDown(object sender, KeyEventArgs e) {
+      if (e.Control && e.KeyCode == Keys.C)
+        CopyValuesFromDataGridView();
+    }
+
+    private void CopyValuesFromDataGridView() {
+      if (DataGridView.SelectedCells.Count == 0) return;
+      StringBuilder s = new StringBuilder();
+      int minRowIndex = DataGridView.SelectedCells[0].RowIndex;
+      int maxRowIndex = DataGridView.SelectedCells[DataGridView.SelectedCells.Count - 1].RowIndex;
+      int minColIndex = DataGridView.SelectedCells[0].ColumnIndex;
+      int maxColIndex = DataGridView.SelectedCells[DataGridView.SelectedCells.Count - 1].ColumnIndex;
+
+      if (minRowIndex > maxRowIndex) {
+        int temp = minRowIndex;
+        minRowIndex = maxRowIndex;
+        maxRowIndex = temp;
+      }
+      if (minColIndex > maxColIndex) {
+        int temp = minColIndex;
+        minColIndex = maxColIndex;
+        maxColIndex = temp;
+      }
+
+      bool addRowNames = DataGridView.AreAllCellsSelected(false) && currentCorrelation.RowNames.Count() > 0;
+      bool addColumnNames = DataGridView.AreAllCellsSelected(false) && currentCorrelation.ColumnNames.Count() > 0;
+
+      //add colum names
+      if (addColumnNames) {
+        if (addRowNames)
+          s.Append('\t');
+
+        DataGridViewColumn column = DataGridView.Columns.GetFirstColumn(DataGridViewElementStates.Visible);
+        while (column != null) {
+          s.Append(column.HeaderText);
+          s.Append('\t');
+          column = DataGridView.Columns.GetNextColumn(column, DataGridViewElementStates.Visible, DataGridViewElementStates.None);
+        }
+        s.Remove(s.Length - 1, 1); //remove last tab
+        s.Append(Environment.NewLine);
+      }
+
+      for (int i = minRowIndex; i <= maxRowIndex; i++) {
+        int rowIndex = this.virtualRowIndices[i];
+        if (addRowNames) {
+          s.Append(currentCorrelation.RowNames.ElementAt(rowIndex));
+          s.Append('\t');
+        }
+
+        DataGridViewColumn column = DataGridView.Columns.GetFirstColumn(DataGridViewElementStates.Visible);
+        while (column != null) {
+          DataGridViewCell cell = DataGridView[column.Index, i];
+          if (cell.Selected) {
+            s.Append(currentCorrelation[rowIndex, column.Index]);
+            s.Append('\t');
+          }
+
+          column = DataGridView.Columns.GetNextColumn(column, DataGridViewElementStates.Visible, DataGridViewElementStates.None);
+        }
+        s.Remove(s.Length - 1, 1); //remove last tab
+        s.Append(Environment.NewLine);
+      }
+      Clipboard.SetText(s.ToString());
+    }
+    #endregion
+
+    protected void ShowHideColumns_Click(object sender, EventArgs e) {
+      variableVisibility.ShowDialog();
+    }
+
+    protected void DataGridView_MouseClick(object sender, MouseEventArgs e) {
+      if (Content == null) return;
+      if (e.Button == MouseButtons.Right && DataGridView.Columns.Count != 0)
+        contextMenu.Show(MousePosition);
+    }
+
+    protected int GetRowIndexOfVirtualindex(int virtualIndex) {
+      if (virtualIndex < 0 || virtualIndex >= virtualRowIndices.Length) {
+        throw new ArgumentException("Virtual index is out of bounds");
+      }
+
+      for (int i = 0; i < virtualRowIndices.Length; i++) {
+        if (virtualRowIndices[i] == virtualIndex) {
+          return i;
+        }
+      }
+      throw new ArgumentException("Virtual index was not found!");
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.resx
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.resx	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/AbstractFeatureCorrelationView.resx	(revision 8811)
@@ -0,0 +1,1025 @@
+﻿<?xml version="1.0" encoding="utf-8"?>
+<root>
+  <!-- 
+    Microsoft ResX Schema 
+    
+    Version 2.0
+    
+    The primary goals of this format is to allow a simple XML format 
+    that is mostly human readable. The generation and parsing of the 
+    various data types are done through the TypeConverter classes 
+    associated with the data types.
+    
+    Example:
+    
+    ... ado.net/XML headers & schema ...
+    <resheader name="resmimetype">text/microsoft-resx</resheader>
+    <resheader name="version">2.0</resheader>
+    <resheader name="reader">System.Resources.ResXResourceReader, System.Windows.Forms, ...</resheader>
+    <resheader name="writer">System.Resources.ResXResourceWriter, System.Windows.Forms, ...</resheader>
+    <data name="Name1"><value>this is my long string</value><comment>this is a comment</comment></data>
+    <data name="Color1" type="System.Drawing.Color, System.Drawing">Blue</data>
+    <data name="Bitmap1" mimetype="application/x-microsoft.net.object.binary.base64">
+        <value>[base64 mime encoded serialized .NET Framework object]</value>
+    </data>
+    <data name="Icon1" type="System.Drawing.Icon, System.Drawing" mimetype="application/x-microsoft.net.object.bytearray.base64">
+        <value>[base64 mime encoded string representing a byte array form of the .NET Framework object]</value>
+        <comment>This is a comment</comment>
+    </data>
+                
+    There are any number of "resheader" rows that contain simple 
+    name/value pairs.
+    
+    Each data row contains a name, and value. The row also contains a 
+    type or mimetype. Type corresponds to a .NET class that support 
+    text/value conversion through the TypeConverter architecture. 
+    Classes that don't support this are serialized and stored with the 
+    mimetype set.
+    
+    The mimetype is used for serialized objects, and tells the 
+    ResXResourceReader how to depersist the object. This is currently not 
+    extensible. For a given mimetype the value must be set accordingly:
+    
+    Note - application/x-microsoft.net.object.binary.base64 is the format 
+    that the ResXResourceWriter will generate, however the reader can 
+    read any of the formats listed below.
+    
+    mimetype: application/x-microsoft.net.object.binary.base64
+    value   : The object must be serialized with 
+            : System.Runtime.Serialization.Formatters.Binary.BinaryFormatter
+            : and then encoded with base64 encoding.
+    
+    mimetype: application/x-microsoft.net.object.soap.base64
+    value   : The object must be serialized with 
+            : System.Runtime.Serialization.Formatters.Soap.SoapFormatter
+            : and then encoded with base64 encoding.
+
+    mimetype: application/x-microsoft.net.object.bytearray.base64
+    value   : The object must be serialized into a byte array 
+            : using a System.ComponentModel.TypeConverter
+            : and then encoded with base64 encoding.
+    -->
+  <xsd:schema id="root" xmlns="" xmlns:xsd="http://www.w3.org/2001/XMLSchema" xmlns:msdata="urn:schemas-microsoft-com:xml-msdata">
+    <xsd:import namespace="http://www.w3.org/XML/1998/namespace" />
+    <xsd:element name="root" msdata:IsDataSet="true">
+      <xsd:complexType>
+        <xsd:choice maxOccurs="unbounded">
+          <xsd:element name="metadata">
+            <xsd:complexType>
+              <xsd:sequence>
+                <xsd:element name="value" type="xsd:string" minOccurs="0" />
+              </xsd:sequence>
+              <xsd:attribute name="name" use="required" type="xsd:string" />
+              <xsd:attribute name="type" type="xsd:string" />
+              <xsd:attribute name="mimetype" type="xsd:string" />
+              <xsd:attribute ref="xml:space" />
+            </xsd:complexType>
+          </xsd:element>
+          <xsd:element name="assembly">
+            <xsd:complexType>
+              <xsd:attribute name="alias" type="xsd:string" />
+              <xsd:attribute name="name" type="xsd:string" />
+            </xsd:complexType>
+          </xsd:element>
+          <xsd:element name="data">
+            <xsd:complexType>
+              <xsd:sequence>
+                <xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
+                <xsd:element name="comment" type="xsd:string" minOccurs="0" msdata:Ordinal="2" />
+              </xsd:sequence>
+              <xsd:attribute name="name" type="xsd:string" use="required" msdata:Ordinal="1" />
+              <xsd:attribute name="type" type="xsd:string" msdata:Ordinal="3" />
+              <xsd:attribute name="mimetype" type="xsd:string" msdata:Ordinal="4" />
+              <xsd:attribute ref="xml:space" />
+            </xsd:complexType>
+          </xsd:element>
+          <xsd:element name="resheader">
+            <xsd:complexType>
+              <xsd:sequence>
+                <xsd:element name="value" type="xsd:string" minOccurs="0" msdata:Ordinal="1" />
+              </xsd:sequence>
+              <xsd:attribute name="name" type="xsd:string" use="required" />
+            </xsd:complexType>
+          </xsd:element>
+        </xsd:choice>
+      </xsd:complexType>
+    </xsd:element>
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+  <resheader name="resmimetype">
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+  </resheader>
+  <resheader name="version">
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+        A/8AA/8AA/8AA/8AA/8AA/8A
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+  </data>
+  <metadata name="contextMenu.TrayLocation" type="System.Drawing.Point, System.Drawing, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b03f5f7f11d50a3a">
+    <value>17, 17</value>
+  </metadata>
+</root>
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCache.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCache.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCache.cs	(revision 8811)
@@ -0,0 +1,58 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.PluginInfrastructure;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [NonDiscoverableType]
+  internal class FeatureCorrelationCache : Object {
+    private Dictionary<FeatureCorrelationEnums.CorrelationCalculators, Dictionary<FeatureCorrelationEnums.Partitions, double[,]>> correlationsCache;
+
+    public FeatureCorrelationCache()
+      : base() {
+      InitializeCaches();
+    }
+
+    private void InitializeCaches() {
+      correlationsCache = new Dictionary<FeatureCorrelationEnums.CorrelationCalculators, Dictionary<FeatureCorrelationEnums.Partitions, double[,]>>();
+      foreach (var calc in FeatureCorrelationEnums.EnumToList<FeatureCorrelationEnums.CorrelationCalculators>()) {
+        correlationsCache.Add(calc, new Dictionary<FeatureCorrelationEnums.Partitions, double[,]>());
+      }
+    }
+
+    public void Reset() {
+      InitializeCaches();
+    }
+
+    public double[,] GetCorrelation(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition) {
+      double[,] corr;
+      correlationsCache[calc].TryGetValue(partition, out corr);
+      return corr;
+    }
+
+    public void SetCorrelation(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition, double[,] correlation) {
+      correlationsCache[calc][partition] = correlation;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCalculator.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationCalculator.cs	(revision 8811)
@@ -0,0 +1,272 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Linq;
+using HeuristicLab.PluginInfrastructure;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [NonDiscoverableType]
+  public class FeatureCorrelationCalculator : Object {
+
+    private BackgroundWorker bw;
+    private BackgroundWorkerInfo bwInfo;
+
+    private IDataAnalysisProblemData problemData;
+    public IDataAnalysisProblemData ProblemData {
+      set {
+        if (bw != null) {
+          bw.CancelAsync();
+        }
+        problemData = value;
+      }
+    }
+
+    public FeatureCorrelationCalculator()
+      : base() { }
+
+    public FeatureCorrelationCalculator(IDataAnalysisProblemData problemData)
+      : base() {
+      this.problemData = problemData;
+    }
+
+    public void CalculateElements(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition) {
+      CalculateElements(problemData.Dataset, calc, partition);
+    }
+
+    // returns if any calculation takes place
+    public bool CalculateTimeframeElements(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition, string variable, int frames, double[,] correlation = null) {
+      if (correlation == null || correlation.GetLength(1) <= frames) {
+        CalculateElements(problemData.Dataset, calc, partition, variable, frames, correlation);
+        return true;
+      } else {
+        return false;
+      }
+    }
+
+    private double[,] GetElementsOfCorrelation(double[,] corr, int frames) {
+      double[,] elements = new double[corr.GetLength(0), frames + 1];
+      for (int i = 0; i < corr.GetLength(0); i++) {
+        for (int j = 0; j <= frames; j++) {
+          elements[i, j] = corr[i, j];
+        }
+      }
+      return elements;
+    }
+
+    private void CalculateElements(Dataset dataset, FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition, string variable = null, int frames = 0, double[,] alreadyCalculated = null) {
+      bwInfo = new BackgroundWorkerInfo { Dataset = dataset, Calculator = calc, Partition = partition, Variable = variable, Frames = frames, AlreadyCalculated = alreadyCalculated };
+      if (bw == null) {
+        bw = new BackgroundWorker();
+        bw.WorkerReportsProgress = true;
+        bw.WorkerSupportsCancellation = true;
+        bw.DoWork += new DoWorkEventHandler(BwDoWork);
+        bw.ProgressChanged += new ProgressChangedEventHandler(BwProgressChanged);
+        bw.RunWorkerCompleted += new RunWorkerCompletedEventHandler(BwRunWorkerCompleted);
+      }
+      if (bw.IsBusy) {
+        bw.CancelAsync();
+      } else {
+        bw.RunWorkerAsync(bwInfo);
+      }
+    }
+
+    #region backgroundworker
+    private void BwDoWork(object sender, DoWorkEventArgs e) {
+      BackgroundWorkerInfo bwInfo = (BackgroundWorkerInfo)e.Argument;
+      if (bwInfo.Variable == null) {
+        BwCalculateCorrelation(sender, e);
+      } else {
+        BwCalculateTimeframeCorrelation(sender, e);
+      }
+    }
+
+    private void BwCalculateCorrelation(object sender, DoWorkEventArgs e) {
+      BackgroundWorker worker = sender as BackgroundWorker;
+
+      BackgroundWorkerInfo bwInfo = (BackgroundWorkerInfo)e.Argument;
+      Dataset dataset = bwInfo.Dataset;
+      FeatureCorrelationEnums.Partitions partition = bwInfo.Partition;
+      FeatureCorrelationEnums.CorrelationCalculators calc = bwInfo.Calculator;
+
+      IList<string> doubleVariableNames = dataset.DoubleVariables.ToList();
+      OnlineCalculatorError error = OnlineCalculatorError.None;
+      int length = doubleVariableNames.Count;
+      double[,] elements = new double[length, length];
+      double calculations = (Math.Pow(length, 2) + length) / 2;
+
+      worker.ReportProgress(0);
+
+      for (int i = 0; i < length; i++) {
+        for (int j = 0; j < i + 1; j++) {
+          if (worker.CancellationPending) {
+            e.Cancel = true;
+            return;
+          }
+          IEnumerable<double> var1 = GetRelevantValues(problemData, partition, doubleVariableNames[i]);
+          IEnumerable<double> var2 = GetRelevantValues(problemData, partition, doubleVariableNames[j]);
+
+          elements[i, j] = CalculateElementWithCalculator(calc, var1, var2, out error);
+
+          if (!error.Equals(OnlineCalculatorError.None)) {
+            elements[i, j] = double.NaN;
+          }
+          elements[j, i] = elements[i, j];
+          worker.ReportProgress((int)Math.Round((((Math.Pow(i, 2) + i) / 2 + j + 1.0) / calculations) * 100));
+        }
+      }
+      e.Result = elements;
+    }
+
+    private void BwCalculateTimeframeCorrelation(object sender, DoWorkEventArgs e) {
+      BackgroundWorker worker = sender as BackgroundWorker;
+
+      BackgroundWorkerInfo bwInfo = (BackgroundWorkerInfo)e.Argument;
+      Dataset dataset = bwInfo.Dataset;
+      FeatureCorrelationEnums.Partitions partition = bwInfo.Partition;
+      FeatureCorrelationEnums.CorrelationCalculators calc = bwInfo.Calculator;
+      string variable = bwInfo.Variable;
+      int frames = bwInfo.Frames;
+      double[,] alreadyCalculated = bwInfo.AlreadyCalculated;
+
+      IList<string> doubleVariableNames = dataset.DoubleVariables.ToList();
+      OnlineCalculatorError error = OnlineCalculatorError.None;
+      int length = doubleVariableNames.Count;
+      double[,] elements = new double[length, frames + 1];
+      double calculations = (frames + 1) * length;
+
+      worker.ReportProgress(0);
+
+      int start = 0;
+      if (alreadyCalculated != null) {
+        for (int i = 0; i < alreadyCalculated.GetLength(0); i++) {
+          Array.Copy(alreadyCalculated, i * alreadyCalculated.GetLength(1), elements, i * elements.GetLength(1), alreadyCalculated.GetLength(1));
+        }
+        start = alreadyCalculated.GetLength(1);
+      }
+
+      for (int i = 0; i < length; i++) {
+        for (int j = start; j <= frames; j++) {
+          if (worker.CancellationPending) {
+            e.Cancel = true;
+            return;
+          }
+
+          IEnumerable<double> var1 = GetRelevantValues(problemData, partition, variable);
+          IEnumerable<double> var2 = GetRelevantValues(problemData, partition, doubleVariableNames[i]);
+
+          var valuesInFrame = var1.Take(j);
+          var help = var1.Skip(j).ToList();
+          help.AddRange(valuesInFrame);
+          var1 = help;
+
+          elements[i, j] = CalculateElementWithCalculator(calc, var1, var2, out error);
+
+          if (!error.Equals(OnlineCalculatorError.None)) {
+            elements[i, j] = double.NaN;
+          }
+          worker.ReportProgress((int)((100.0 / calculations) * (i * (frames + 1) + j + 1)));
+        }
+      }
+      e.Result = elements;
+    }
+
+    private IEnumerable<double> GetRelevantValues(IDataAnalysisProblemData problemData, FeatureCorrelationEnums.Partitions partition, string variable) {
+      IEnumerable<double> var = problemData.Dataset.GetDoubleValues(variable);
+      if (partition.Equals(FeatureCorrelationEnums.Partitions.TrainingSamples)) {
+        var = var.Skip(problemData.TrainingPartition.Start).Take(problemData.TrainingPartition.End - problemData.TrainingPartition.Start);
+      } else if (partition.Equals(FeatureCorrelationEnums.Partitions.TestSamples)) {
+        var = var.Skip(problemData.TestPartition.Start).Take(problemData.TestPartition.End - problemData.TestPartition.Start);
+      }
+      return var;
+    }
+
+    private double CalculateElementWithCalculator(FeatureCorrelationEnums.CorrelationCalculators calc, IEnumerable<double> var1, IEnumerable<double> var2, out OnlineCalculatorError error) {
+      if (calc.Equals(FeatureCorrelationEnums.CorrelationCalculators.HoeffdingsDependence)) {
+        return HoeffdingsDependenceCalculator.Calculate(var1, var2, out error);
+      } else if (calc.Equals(FeatureCorrelationEnums.CorrelationCalculators.SpearmansRank)) {
+        return SpearmansRankCorrelationCoefficientCalculator.Calculate(var1, var2, out error);
+      } else if (calc.Equals(FeatureCorrelationEnums.CorrelationCalculators.PearsonsRSquared)) {
+        return OnlinePearsonsRSquaredCalculator.Calculate(var1, var2, out error);
+      } else {
+        return Math.Sqrt(OnlinePearsonsRSquaredCalculator.Calculate(var1, var2, out error));
+      }
+    }
+
+    private void BwRunWorkerCompleted(object sender, RunWorkerCompletedEventArgs e) {
+      BackgroundWorker worker = sender as BackgroundWorker;
+      if (!e.Cancelled && !worker.CancellationPending) {
+        if (!(e.Error == null)) {
+          ErrorHandling.ShowErrorDialog(e.Error);
+        } else {
+          OnCorrelationCalculationFinished((double[,])e.Result, bwInfo.Calculator, bwInfo.Partition, bwInfo.Variable);
+        }
+      } else {
+        bw.RunWorkerAsync(bwInfo);
+      }
+    }
+    #endregion
+
+    #region events
+    public class CorrelationCalculationFinishedArgs : EventArgs {
+      public double[,] Correlation { get; private set; }
+      public FeatureCorrelationEnums.CorrelationCalculators Calculcator { get; private set; }
+      public FeatureCorrelationEnums.Partitions Partition { get; private set; }
+      public string Variable { get; private set; }
+
+      public CorrelationCalculationFinishedArgs(double[,] correlation, FeatureCorrelationEnums.CorrelationCalculators calculator, FeatureCorrelationEnums.Partitions partition, string variable = null) {
+        this.Correlation = correlation;
+        this.Calculcator = calculator;
+        this.Partition = partition;
+        this.Variable = variable;
+      }
+    }
+
+    public delegate void CorrelationCalculationFinishedHandler(object sender, CorrelationCalculationFinishedArgs e);
+    public event CorrelationCalculationFinishedHandler CorrelationCalculationFinished;
+    protected virtual void OnCorrelationCalculationFinished(double[,] correlation, FeatureCorrelationEnums.CorrelationCalculators calculator, FeatureCorrelationEnums.Partitions partition, string variable = null) {
+      var handler = CorrelationCalculationFinished;
+      if (handler != null)
+        handler(this, new CorrelationCalculationFinishedArgs(correlation, calculator, partition, variable));
+    }
+
+    public delegate void ProgressCalculationHandler(object sender, ProgressChangedEventArgs e);
+    public event ProgressCalculationHandler ProgressCalculation;
+    protected void BwProgressChanged(object sender, ProgressChangedEventArgs e) {
+      BackgroundWorker worker = sender as BackgroundWorker;
+      if (!worker.CancellationPending && ProgressCalculation != null) {
+        ProgressCalculation(sender, e);
+      }
+    }
+    #endregion
+
+    private class BackgroundWorkerInfo {
+      public Dataset Dataset { get; set; }
+      public FeatureCorrelationEnums.CorrelationCalculators Calculator { get; set; }
+      public FeatureCorrelationEnums.Partitions Partition { get; set; }
+      public string Variable { get; set; }
+      public int Frames { get; set; }
+      public double[,] AlreadyCalculated { get; set; }
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationEnums.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationEnums.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationEnums.cs	(revision 8811)
@@ -0,0 +1,103 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.ComponentModel;
+using System.Linq;
+using System.Reflection;
+using HeuristicLab.Data;
+using HeuristicLab.PluginInfrastructure;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  // see http://blog.spontaneouspublicity.com/associating-strings-with-enums-in-c
+  [NonDiscoverableType]
+  public class FeatureCorrelationEnums {
+    public enum Partitions {
+      [Description("All Samples")]
+      AllSamples,
+      [Description("Training Samples")]
+      TrainingSamples,
+      [Description("Test Samples")]
+      TestSamples
+    }
+
+    public enum CorrelationCalculators {
+      [Description("Pearsons R")]
+      PearsonsR,
+      [Description("Pearsons R Squared")]
+      PearsonsRSquared,
+      [Description("Hoeffdings Dependence")]
+      HoeffdingsDependence,
+      [Description("Spearmans Rank")]
+      SpearmansRank
+    }
+
+    public static readonly Dictionary<CorrelationCalculators, DoubleRange> calculatorInterval = new Dictionary<CorrelationCalculators, DoubleRange>(){ 
+      { CorrelationCalculators.PearsonsR, new DoubleRange(1.0, -1.0)},
+      { CorrelationCalculators.SpearmansRank, new DoubleRange(1.0, -1.0)},
+      { CorrelationCalculators.HoeffdingsDependence, new DoubleRange(1.0, -0.5)},
+      { CorrelationCalculators.PearsonsRSquared, new DoubleRange(1.0, 0.0)}
+    };
+
+    public static string GetEnumDescription(object value) {
+      FieldInfo fi = value.GetType().GetField(value.ToString());
+
+      DescriptionAttribute[] attributes =
+          (DescriptionAttribute[])fi.GetCustomAttributes(typeof(DescriptionAttribute), false);
+
+      if (attributes != null && attributes.Length > 0)
+        return attributes[0].Description;
+      else
+        return value.ToString();
+    }
+
+    public static IEnumerable<T> EnumToList<T>() {
+      Type enumType = typeof(T);
+
+      // Can't use generic type constraints on value types,
+      // so have to do check like this
+      if (enumType.BaseType != typeof(Enum)) {
+        throw new ArgumentException("T must be of type System.Enum");
+      }
+
+      Array enumValArray = Enum.GetValues(enumType);
+      return Enum.GetValues(enumType).Cast<int>().Select(x => (T)Enum.Parse(enumType, x.ToString())).ToList();
+    }
+
+    public static T GetEnumOfDescription<T>(string desc) {
+      Type enumType = typeof(T);
+
+      if (enumType.BaseType != typeof(Enum))
+        throw new ArgumentException("T must be of type System.Enum");
+
+      Array enumValArray = Enum.GetValues(enumType);
+      foreach (int val in enumValArray) {
+
+        T e = (T)Enum.Parse(enumType, val.ToString());
+        if (GetEnumDescription(e).Equals(desc)) {
+          return (T)e;
+        }
+      }
+      throw new ArgumentException("Description is not in the given type T");
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationTimeframeCache.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationTimeframeCache.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationTimeframeCache.cs	(revision 8811)
@@ -0,0 +1,61 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.PluginInfrastructure;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [NonDiscoverableType]
+  internal class FeatureCorrelationTimeframeCache : Object {
+    private Dictionary<FeatureCorrelationEnums.CorrelationCalculators, Dictionary<FeatureCorrelationEnums.Partitions, Dictionary<string, double[,]>>> timeFrameCorrelationsCache;
+
+    public FeatureCorrelationTimeframeCache()
+      : base() {
+      InitializeCaches();
+    }
+
+    private void InitializeCaches() {
+      timeFrameCorrelationsCache = new Dictionary<FeatureCorrelationEnums.CorrelationCalculators, Dictionary<FeatureCorrelationEnums.Partitions, Dictionary<string, double[,]>>>();
+      foreach (var calc in FeatureCorrelationEnums.EnumToList<FeatureCorrelationEnums.CorrelationCalculators>()) {
+        timeFrameCorrelationsCache.Add(calc, new Dictionary<FeatureCorrelationEnums.Partitions, Dictionary<string, double[,]>>());
+        foreach (var part in FeatureCorrelationEnums.EnumToList<FeatureCorrelationEnums.Partitions>()) {
+          timeFrameCorrelationsCache[calc].Add(part, new Dictionary<string, double[,]>());
+        }
+      }
+    }
+
+    public void Reset() {
+      InitializeCaches();
+    }
+
+    public double[,] GetTimeframeCorrelation(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition, string variable) {
+      double[,] corr;
+      timeFrameCorrelationsCache[calc][partition].TryGetValue(variable, out corr);
+      return corr;
+    }
+
+    public void SetTimeframeCorrelation(FeatureCorrelationEnums.CorrelationCalculators calc, FeatureCorrelationEnums.Partitions partition, string variable, double[,] correlation) {
+      timeFrameCorrelationsCache[calc][partition][variable] = correlation;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.Designer.cs	(revision 8811)
@@ -0,0 +1,60 @@
+﻿#region License Information
+
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#endregion
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class FeatureCorrelationView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.SuspendLayout();
+      // 
+      // FeatureCorrelationView
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+      this.Name = "FeatureCorrelationView";
+      this.Size = new System.Drawing.Size(569, 336);
+      this.ResumeLayout(false);
+    }
+    #endregion
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/FeatureCorrelationView.cs	(revision 8811)
@@ -0,0 +1,85 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Windows.Forms;
+using HeuristicLab.Analysis;
+using HeuristicLab.Data;
+using HeuristicLab.MainForm;
+using HeuristicLab.MainForm.WindowsForms;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Feature Correlation View")]
+  [Content(typeof(DataAnalysisProblemData), false)]
+  public partial class FeatureCorrelationView : AbstractFeatureCorrelationView {
+
+    private FeatureCorrelationCache correlationCache;
+
+    public FeatureCorrelationView()
+      : base() {
+      InitializeComponent();
+      correlationCache = new FeatureCorrelationCache();
+    }
+
+    protected override void OnContentChanged() {
+      correlationCache.Reset();
+      base.OnContentChanged();
+    }
+
+    protected override void CalculateCorrelation() {
+      if (CorrelationCalcComboBox.SelectedItem != null && PartitionComboBox.SelectedItem != null) {
+        FeatureCorrelationEnums.CorrelationCalculators calc = (FeatureCorrelationEnums.CorrelationCalculators)CorrelationCalcComboBox.SelectedValue;
+        FeatureCorrelationEnums.Partitions partition = (FeatureCorrelationEnums.Partitions)PartitionComboBox.SelectedValue;
+        DataGridView.Columns.Clear();
+        DataGridView.Enabled = false;
+        double[,] corr = correlationCache.GetCorrelation(calc, partition);
+        if (corr == null) {
+          fcc.CalculateElements(calc, partition);
+        } else {
+          SetNewCorrelation(corr, calc);
+          UpdateDataGrid();
+        }
+      }
+    }
+
+    private void SetNewCorrelation(double[,] elements, FeatureCorrelationEnums.CorrelationCalculators calc) {
+      DoubleRange range = FeatureCorrelationEnums.calculatorInterval[calc];
+      HeatMap hm = new HeatMap(elements, "", range.End, range.Start);
+      hm.RowNames = Content.Dataset.DoubleVariables;
+      hm.ColumnNames = Content.Dataset.DoubleVariables;
+      currentCorrelation = hm;
+    }
+
+    protected override void Content_CorrelationCalculationFinished(object sender, FeatureCorrelationCalculator.CorrelationCalculationFinishedArgs e) {
+      if (InvokeRequired) {
+        Invoke(new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished), sender, e);
+      } else {
+        correlationCache.SetCorrelation(e.Calculcator, e.Partition, e.Correlation);
+        SetNewCorrelation(e.Correlation, e.Calculcator);
+        UpdateDataGrid();
+      }
+    }
+
+    protected override void variableVisibility_VariableVisibilityChanged(object sender, ItemCheckEventArgs e) {
+      DataGridView.Columns[e.Index].Visible = e.NewValue == CheckState.Checked;
+      DataGridView.Rows[GetRowIndexOfVirtualindex(e.Index)].Visible = e.NewValue == CheckState.Checked;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.Designer.cs	(revision 8811)
@@ -0,0 +1,150 @@
+﻿#region License Information
+
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#endregion
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class TimeframeFeatureCorrelationView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.VariableSelectionLabel = new System.Windows.Forms.Label();
+      this.VariableSelectionComboBox = new System.Windows.Forms.ComboBox();
+      this.TimeFrameLabel = new System.Windows.Forms.Label();
+      this.TimeframeComboBox = new System.Windows.Forms.ComboBox();
+      ((System.ComponentModel.ISupportInitialize)(this.PictureBox)).BeginInit();
+      ((System.ComponentModel.ISupportInitialize)(this.SplitContainer)).BeginInit();
+      this.SplitContainer.Panel1.SuspendLayout();
+      this.SplitContainer.Panel2.SuspendLayout();
+      this.SplitContainer.SuspendLayout();
+      this.CalculatingPanel.SuspendLayout();
+      this.SuspendLayout();
+      // 
+      // PartitionComboBox
+      // 
+      this.PartitionComboBox.Location = new System.Drawing.Point(344, 3);
+      this.PartitionComboBox.Size = new System.Drawing.Size(131, 21);
+      // 
+      // maximumLabel
+      // 
+      this.maximumLabel.Location = new System.Drawing.Point(487, 3);
+      // 
+      // PictureBox
+      // 
+      this.PictureBox.Location = new System.Drawing.Point(506, 31);
+      this.PictureBox.Size = new System.Drawing.Size(35, 280);
+      // 
+      // SplitContainer
+      // 
+      // 
+      // SplitContainer.Panel1
+      // 
+      this.SplitContainer.Panel1.Controls.Add(this.VariableSelectionComboBox);
+      this.SplitContainer.Panel1.Controls.Add(this.TimeframeComboBox);
+      this.SplitContainer.Panel1.Controls.Add(this.TimeFrameLabel);
+      this.SplitContainer.Panel1.Controls.Add(this.VariableSelectionLabel);
+      this.SplitContainer.SplitterDistance = 52;
+      // 
+      // CalculatingPanel
+      // 
+      this.CalculatingPanel.Location = new System.Drawing.Point(138, 82);
+      // 
+      // VariableSelectionLabel
+      // 
+      this.VariableSelectionLabel.AutoSize = true;
+      this.VariableSelectionLabel.Location = new System.Drawing.Point(0, 33);
+      this.VariableSelectionLabel.Name = "VariableSelectionLabel";
+      this.VariableSelectionLabel.Size = new System.Drawing.Size(81, 13);
+      this.VariableSelectionLabel.TabIndex = 16;
+      this.VariableSelectionLabel.Text = "Select Variable:";
+      // 
+      // VariableSelectionComboBox
+      // 
+      this.VariableSelectionComboBox.DropDownStyle = System.Windows.Forms.ComboBoxStyle.DropDownList;
+      this.VariableSelectionComboBox.FormattingEnabled = true;
+      this.VariableSelectionComboBox.Location = new System.Drawing.Point(110, 30);
+      this.VariableSelectionComboBox.Name = "VariableSelectionComboBox";
+      this.VariableSelectionComboBox.Size = new System.Drawing.Size(163, 21);
+      this.VariableSelectionComboBox.TabIndex = 17;
+      this.VariableSelectionComboBox.SelectionChangeCommitted += new System.EventHandler(this.VariableSelectionComboBox_SelectedChangeCommitted);
+      // 
+      // TimeFrameLabel
+      // 
+      this.TimeFrameLabel.AutoSize = true;
+      this.TimeFrameLabel.Location = new System.Drawing.Point(279, 33);
+      this.TimeFrameLabel.Name = "TimeFrameLabel";
+      this.TimeFrameLabel.Size = new System.Drawing.Size(59, 13);
+      this.TimeFrameLabel.TabIndex = 18;
+      this.TimeFrameLabel.Text = "Timeframe:";
+      // 
+      // TimeframeComboBox
+      // 
+      this.TimeframeComboBox.DropDownStyle = System.Windows.Forms.ComboBoxStyle.DropDownList;
+      this.TimeframeComboBox.FormattingEnabled = true;
+      this.TimeframeComboBox.Location = new System.Drawing.Point(344, 30);
+      this.TimeframeComboBox.Name = "TimeframeComboBox";
+      this.TimeframeComboBox.Size = new System.Drawing.Size(131, 21);
+      this.TimeframeComboBox.TabIndex = 19;
+      this.TimeframeComboBox.SelectionChangeCommitted += new System.EventHandler(this.TimeframeComboBox_SelectedChangeCommitted);
+      // 
+      // TimeframeFeatureCorrelationView
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+      this.Name = "TimeframeFeatureCorrelationView";
+      ((System.ComponentModel.ISupportInitialize)(this.PictureBox)).EndInit();
+      this.SplitContainer.Panel1.ResumeLayout(false);
+      this.SplitContainer.Panel1.PerformLayout();
+      this.SplitContainer.Panel2.ResumeLayout(false);
+      ((System.ComponentModel.ISupportInitialize)(this.SplitContainer)).EndInit();
+      this.SplitContainer.ResumeLayout(false);
+      this.CalculatingPanel.ResumeLayout(false);
+      this.CalculatingPanel.PerformLayout();
+      this.ResumeLayout(false);
+
+    }
+    #endregion
+
+    protected System.Windows.Forms.Label VariableSelectionLabel;
+    protected System.Windows.Forms.ComboBox VariableSelectionComboBox;
+    protected System.Windows.Forms.Label TimeFrameLabel;
+    protected System.Windows.Forms.ComboBox TimeframeComboBox;
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/TimeframeFeatureCorrelationView.cs	(revision 8811)
@@ -0,0 +1,113 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Linq;
+using System.Windows.Forms;
+using HeuristicLab.Analysis;
+using HeuristicLab.Data;
+using HeuristicLab.MainForm;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Timeframe Feature Correlation View")]
+  [Content(typeof(DataAnalysisProblemData), false)]
+  public partial class TimeframeFeatureCorrelationView : AbstractFeatureCorrelationView {
+
+    private FeatureCorrelationTimeframeCache correlationTimeframCache;
+
+    public TimeframeFeatureCorrelationView() {
+      InitializeComponent();
+      TimeframeComboBox.DataSource = Enumerable.Range(0, 16).ToList<int>();
+      correlationTimeframCache = new FeatureCorrelationTimeframeCache();
+    }
+
+    protected override void OnContentChanged() {
+      correlationTimeframCache.Reset();
+      if (Content != null) {
+        VariableSelectionComboBox.DataSource = Content.Dataset.DoubleVariables.ToList();
+      }
+      base.OnContentChanged();
+    }
+
+    protected void VariableSelectionComboBox_SelectedChangeCommitted(object sender, EventArgs e) {
+      CalculateCorrelation();
+    }
+    protected void TimeframeComboBox_SelectedChangeCommitted(object sender, EventArgs e) {
+      CalculateCorrelation();
+    }
+
+    protected override void CalculateCorrelation() {
+      string variable = (string)VariableSelectionComboBox.SelectedItem;
+      if (CorrelationCalcComboBox.SelectedItem != null && PartitionComboBox.SelectedItem != null && variable != null) {
+        FeatureCorrelationEnums.CorrelationCalculators calc = (FeatureCorrelationEnums.CorrelationCalculators)CorrelationCalcComboBox.SelectedValue;
+        FeatureCorrelationEnums.Partitions partition = (FeatureCorrelationEnums.Partitions)PartitionComboBox.SelectedValue;
+        DataGridView.Columns.Clear();
+        DataGridView.Enabled = false;
+        int frames = (int)TimeframeComboBox.SelectedItem;
+        double[,] corr = correlationTimeframCache.GetTimeframeCorrelation(calc, partition, variable);
+        if (corr == null) {
+          fcc.CalculateTimeframeElements(calc, partition, variable, frames);
+        } else if (corr.GetLength(1) <= frames) {
+          fcc.CalculateTimeframeElements(calc, partition, variable, frames, corr);
+        } else {
+          SetNewCorrelation(corr, calc, frames);
+          UpdateDataGrid();
+        }
+      }
+    }
+
+    private void SetNewCorrelation(double[,] elements, FeatureCorrelationEnums.CorrelationCalculators calc, int frames) {
+      double[,] neededValues = new double[elements.GetLength(0), frames + 1];
+      for (int i = 0; i < elements.GetLength(0); i++) {
+        Array.Copy(elements, i * elements.GetLength(1), neededValues, i * neededValues.GetLength(1), frames + 1);
+      }
+      SetNewCorrelation(neededValues, calc);
+    }
+
+    private void SetNewCorrelation(double[,] elements, FeatureCorrelationEnums.CorrelationCalculators calc) {
+      DoubleRange range = FeatureCorrelationEnums.calculatorInterval[calc];
+      HeatMap hm = new HeatMap(elements, "", range.End, range.Start);
+      hm.RowNames = Content.Dataset.DoubleVariables;
+      hm.ColumnNames = Enumerable.Range(0, elements.GetLength(1)).Select(x => x.ToString());
+      currentCorrelation = hm;
+    }
+
+    protected override void Content_CorrelationCalculationFinished(object sender, FeatureCorrelationCalculator.CorrelationCalculationFinishedArgs e) {
+      if (InvokeRequired) {
+        Invoke(new FeatureCorrelationCalculator.CorrelationCalculationFinishedHandler(Content_CorrelationCalculationFinished), sender, e);
+      } else {
+        correlationTimeframCache.SetTimeframeCorrelation(e.Calculcator, e.Partition, e.Variable, e.Correlation);
+        SetNewCorrelation(e.Correlation, e.Calculcator);
+        UpdateDataGrid();
+      }
+    }
+
+    protected override void UpdateColumnHeaders() {
+      for (int i = 0; i < DataGridView.ColumnCount; i++) {
+        DataGridView.Columns[i].HeaderText = i.ToString();
+      }
+    }
+
+    protected override void variableVisibility_VariableVisibilityChanged(object sender, ItemCheckEventArgs e) {
+      DataGridView.Rows[GetRowIndexOfVirtualindex(e.Index)].Visible = e.NewValue == CheckState.Checked;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.Designer.cs	(revision 8811)
@@ -0,0 +1,112 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+namespace HeuristicLab.DataAnalysis.Views {
+  partial class VariableVisibilityDialog {
+    /// <summary>
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary>
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Windows Form Designer generated code
+
+    /// <summary>
+    /// Required method for Designer support - do not modify
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.checkedListBox = new System.Windows.Forms.CheckedListBox();
+      this.btnShowAll = new System.Windows.Forms.Button();
+      this.btnHideAll = new System.Windows.Forms.Button();
+      this.SuspendLayout();
+      // 
+      // checkedListBox
+      // 
+      this.checkedListBox.Anchor = ((System.Windows.Forms.AnchorStyles)((((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Bottom)
+                  | System.Windows.Forms.AnchorStyles.Left)
+                  | System.Windows.Forms.AnchorStyles.Right)));
+      this.checkedListBox.CheckOnClick = true;
+      this.checkedListBox.FormattingEnabled = true;
+      this.checkedListBox.Location = new System.Drawing.Point(12, 12);
+      this.checkedListBox.Name = "checkedListBox";
+      this.checkedListBox.Size = new System.Drawing.Size(309, 289);
+      this.checkedListBox.TabIndex = 0;
+      this.checkedListBox.ItemCheck += new System.Windows.Forms.ItemCheckEventHandler(this.checkedListBox_ItemCheck);
+      // 
+      // btnShowAll
+      // 
+      this.btnShowAll.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Bottom | System.Windows.Forms.AnchorStyles.Left)));
+      this.btnShowAll.Location = new System.Drawing.Point(12, 315);
+      this.btnShowAll.Name = "btnShowAll";
+      this.btnShowAll.Size = new System.Drawing.Size(75, 23);
+      this.btnShowAll.TabIndex = 1;
+      this.btnShowAll.Text = "Show all";
+      this.btnShowAll.UseVisualStyleBackColor = true;
+      this.btnShowAll.Click += new System.EventHandler(this.btnShowAll_Click);
+      // 
+      // btnHideAll
+      // 
+      this.btnHideAll.Anchor = ((System.Windows.Forms.AnchorStyles)((System.Windows.Forms.AnchorStyles.Bottom | System.Windows.Forms.AnchorStyles.Left)));
+      this.btnHideAll.Location = new System.Drawing.Point(101, 315);
+      this.btnHideAll.Name = "btnHideAll";
+      this.btnHideAll.Size = new System.Drawing.Size(75, 23);
+      this.btnHideAll.TabIndex = 2;
+      this.btnHideAll.Text = "Hide all";
+      this.btnHideAll.UseVisualStyleBackColor = true;
+      this.btnHideAll.Click += new System.EventHandler(this.btnHideAll_Click);
+      // 
+      // ColumnsVisibilityDialog
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Inherit;
+      this.ClientSize = new System.Drawing.Size(332, 350);
+      this.Controls.Add(this.btnHideAll);
+      this.Controls.Add(this.btnShowAll);
+      this.Controls.Add(this.checkedListBox);
+      this.MaximizeBox = false;
+      this.MinimizeBox = false;
+      this.Name = "ColumnsVisibilityDialog";
+      this.ShowIcon = false;
+      this.ShowInTaskbar = false;
+      this.StartPosition = System.Windows.Forms.FormStartPosition.CenterParent;
+      this.Text = "Show/Hide Columns";
+      this.ResumeLayout(false);
+
+    }
+
+    #endregion
+
+    private System.Windows.Forms.CheckedListBox checkedListBox;
+    private System.Windows.Forms.Button btnShowAll;
+    private System.Windows.Forms.Button btnHideAll;
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/FeatureCorrelation/VariableVisibilityDialog.cs	(revision 8811)
@@ -0,0 +1,76 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Windows.Forms;
+
+namespace HeuristicLab.DataAnalysis.Views {
+  public partial class VariableVisibilityDialog : Form {
+    public VariableVisibilityDialog() {
+      InitializeComponent();
+    }
+
+    public VariableVisibilityDialog(IEnumerable<string> columns, IEnumerable<bool> visibility)
+      : this() {
+      if (columns.Count() != visibility.Count()) {
+        throw new ArgumentException("There have to be as many variables as values for visibility!");
+      }
+      this.columns = columns.ToList();
+      this.visibility = visibility.ToList();
+      UpdateCheckBoxes();
+    }
+
+    private List<string> columns;
+    public IList<string> Columns {
+      get { return this.columns; }
+    }
+    private List<bool> visibility;
+    public IList<bool> Visibility {
+      get { return this.visibility; }
+    }
+
+    private void UpdateCheckBoxes() {
+      for (int i = 0; i < columns.Count; i++) {
+        checkedListBox.Items.Add(columns[i], visibility[i]);
+      }
+    }
+
+    public event ItemCheckEventHandler VariableVisibilityChanged;
+
+    private void checkedListBox_ItemCheck(object sender, ItemCheckEventArgs e) {
+      this.visibility[e.Index] = e.NewValue == CheckState.Checked;
+      if (VariableVisibilityChanged != null) {
+        VariableVisibilityChanged(this, e);
+      }
+    }
+
+    private void btnShowAll_Click(object sender, System.EventArgs e) {
+      for (int i = 0; i < checkedListBox.Items.Count; i++)
+        checkedListBox.SetItemChecked(i, true);
+    }
+    private void btnHideAll_Click(object sender, System.EventArgs e) {
+      for (int i = 0; i < checkedListBox.Items.Count; i++)
+        checkedListBox.SetItemChecked(i, false);
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/HeuristicLab.Problems.DataAnalysis.Views-3.4.csproj
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/HeuristicLab.Problems.DataAnalysis.Views-3.4.csproj	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/HeuristicLab.Problems.DataAnalysis.Views-3.4.csproj	(revision 8811)
@@ -185,4 +185,22 @@
       <DependentUpon>ClassificationEnsembleSolutionModelView.cs</DependentUpon>
     </Compile>
+    <Compile Include="Classification\ClassificationFeatureCorrelationView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="Classification\ClassificationFeatureCorrelationView.Designer.cs">
+      <DependentUpon>ClassificationFeatureCorrelationView.cs</DependentUpon>
+    </Compile>
+    <Compile Include="Classification\ClassificationTimeframeFeatureCorrelationView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="Classification\ClassificationTimeframeFeatureCorrelationView.Designer.cs">
+      <DependentUpon>ClassificationTimeframeFeatureCorrelationView.cs</DependentUpon>
+    </Compile>
+    <Compile Include="Classification\DiscriminantFunctionClassificationModelView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="Classification\DiscriminantFunctionClassificationModelView.Designer.cs">
+      <DependentUpon>DiscriminantFunctionClassificationModelView.cs</DependentUpon>
+    </Compile>
     <Compile Include="Clustering\ClusteringSolutionVisualizationView.cs">
       <SubType>UserControl</SubType>
@@ -191,4 +209,10 @@
       <DependentUpon>ClusteringSolutionVisualizationView.cs</DependentUpon>
     </Compile>
+    <Compile Include="FeatureCorrelation\AbstractFeatureCorrelationView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="FeatureCorrelation\AbstractFeatureCorrelationView.Designer.cs">
+      <DependentUpon>AbstractFeatureCorrelationView.cs</DependentUpon>
+    </Compile>
     <Compile Include="Classification\ClassificationEnsembleSolutionConfidenceAccuracyDependence.cs">
       <SubType>UserControl</SubType>
@@ -209,5 +233,39 @@
       <DependentUpon>DataAnalysisSolutionEvaluationView.cs</DependentUpon>
     </Compile>
+    <Compile Include="FeatureCorrelation\FeatureCorrelationCalculator.cs" />
+    <Compile Include="FeatureCorrelation\FeatureCorrelationEnums.cs" />
+    <Compile Include="FeatureCorrelation\VariableVisibilityDialog.cs">
+      <SubType>Form</SubType>
+    </Compile>
+    <Compile Include="FeatureCorrelation\VariableVisibilityDialog.Designer.cs">
+      <DependentUpon>VariableVisibilityDialog.cs</DependentUpon>
+    </Compile>
+    <Compile Include="FeatureCorrelation\FeatureCorrelationView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="FeatureCorrelation\FeatureCorrelationView.Designer.cs">
+      <DependentUpon>FeatureCorrelationView.cs</DependentUpon>
+    </Compile>
+    <Compile Include="FeatureCorrelation\FeatureCorrelationCache.cs" />
+    <Compile Include="FeatureCorrelation\FeatureCorrelationTimeframeCache.cs" />
     <Compile Include="Plugin.cs" />
+    <Compile Include="ProblemDataView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="ProblemDataView.Designer.cs">
+      <DependentUpon>ProblemDataView.cs</DependentUpon>
+    </Compile>
+    <Compile Include="Regression\RegressionFeatureCorrelationView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="Regression\RegressionFeatureCorrelationView.Designer.cs">
+      <DependentUpon>RegressionFeatureCorrelationView.cs</DependentUpon>
+    </Compile>
+    <Compile Include="Regression\RegressionTimeframeFeatureCorrelationView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="Regression\RegressionTimeframeFeatureCorrelationView.Designer.cs">
+      <DependentUpon>RegressionTimeframeFeatureCorrelationView.cs</DependentUpon>
+    </Compile>
     <Compile Include="Regression\RegressionEnsembleSolutionModelView.cs">
       <SubType>UserControl</SubType>
@@ -337,4 +395,28 @@
     <Compile Include="Solution Views\RegressionSolutionView.Designer.cs">
       <DependentUpon>RegressionSolutionView.cs</DependentUpon>
+    </Compile>
+    <Compile Include="FeatureCorrelation\TimeframeFeatureCorrelationView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="FeatureCorrelation\TimeframeFeatureCorrelationView.Designer.cs">
+      <DependentUpon>TimeframeFeatureCorrelationView.cs</DependentUpon>
+    </Compile>
+    <Compile Include="Solution Views\TimeSeriesPrognosisSolutionView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="Solution Views\TimeSeriesPrognosisSolutionView.Designer.cs">
+      <DependentUpon>TimeSeriesPrognosisSolutionView.cs</DependentUpon>
+    </Compile>
+    <Compile Include="TimeSeriesPrognosis\TimeSeriesPrognosisResultsView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="TimeSeriesPrognosis\TimeSeriesPrognosisResultsView.Designer.cs">
+      <DependentUpon>TimeSeriesPrognosisResultsView.cs</DependentUpon>
+    </Compile>
+    <Compile Include="TimeSeriesPrognosis\TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.cs">
+      <SubType>UserControl</SubType>
+    </Compile>
+    <Compile Include="TimeSeriesPrognosis\TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.Designer.cs">
+      <DependentUpon>TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.cs</DependentUpon>
     </Compile>
     <None Include="HeuristicLab.snk" />
@@ -368,5 +450,7 @@
   </ItemGroup>
   <ItemGroup>
-    <Folder Include="obj\" />
+    <EmbeddedResource Include="FeatureCorrelation\AbstractFeatureCorrelationView.resx">
+      <DependentUpon>AbstractFeatureCorrelationView.cs</DependentUpon>
+    </EmbeddedResource>
   </ItemGroup>
   <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
@@ -379,5 +463,5 @@
   -->
   <PropertyGroup>
-    <PreBuildEvent>set Path=%25Path%25;$(ProjectDir);$(SolutionDir)
+    <PreBuildEvent Condition=" '$(OS)' == 'Windows_NT' ">set Path=%25Path%25;$(ProjectDir);$(SolutionDir)
 set ProjectDir=$(ProjectDir)
 set SolutionDir=$(SolutionDir)
@@ -386,4 +470,10 @@
 call PreBuildEvent.cmd
 </PreBuildEvent>
+    <PreBuildEvent Condition=" '$(OS)' != 'Windows_NT' ">
+export ProjectDir=$(ProjectDir)
+export SolutionDir=$(SolutionDir)
+
+$SolutionDir/PreBuildEvent.sh
+</PreBuildEvent>
   </PropertyGroup>
 </Project>
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Plugin.cs.frame
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Plugin.cs.frame	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Plugin.cs.frame	(revision 8811)
@@ -47,2 +47,3 @@
   }
 }
+
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/ProblemDataView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/ProblemDataView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/ProblemDataView.Designer.cs	(revision 8811)
@@ -0,0 +1,74 @@
+﻿namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class ProblemDataView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.FeatureCorrelationButton = new System.Windows.Forms.Button();
+      ((System.ComponentModel.ISupportInitialize)(this.errorProvider)).BeginInit();
+      this.SuspendLayout();
+      // 
+      // parameterCollectionView
+      // 
+      this.parameterCollectionView.AllowDrop = true;
+      this.parameterCollectionView.DragDrop += new System.Windows.Forms.DragEventHandler(this.parameterCollectionView_DragDrop);
+      this.parameterCollectionView.DragEnter += new System.Windows.Forms.DragEventHandler(this.parameterCollectionView_DragEnterOver);
+      this.parameterCollectionView.DragOver += new System.Windows.Forms.DragEventHandler(this.parameterCollectionView_DragEnterOver);
+      // 
+      // nameTextBox
+      // 
+      this.errorProvider.SetIconAlignment(this.nameTextBox, System.Windows.Forms.ErrorIconAlignment.MiddleLeft);
+      this.errorProvider.SetIconPadding(this.nameTextBox, 2);
+      // 
+      // FeatureCorrelationButton
+      // 
+      this.FeatureCorrelationButton.Location = new System.Drawing.Point(184, 45);
+      this.FeatureCorrelationButton.Name = "FeatureCorrelationButton";
+      this.FeatureCorrelationButton.Size = new System.Drawing.Size(121, 24);
+      this.FeatureCorrelationButton.TabIndex = 4;
+      this.FeatureCorrelationButton.Text = "Feature Correlation";
+      this.FeatureCorrelationButton.UseVisualStyleBackColor = true;
+      this.FeatureCorrelationButton.Click += new System.EventHandler(this.FeatureCorrelationButton_Click);
+      // 
+      // ProblemDataView
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+      this.Controls.Add(this.FeatureCorrelationButton);
+      this.Name = "ProblemDataView";
+      this.Controls.SetChildIndex(this.parameterCollectionView, 0);
+      this.Controls.SetChildIndex(this.infoLabel, 0);
+      this.Controls.SetChildIndex(this.nameTextBox, 0);
+      this.Controls.SetChildIndex(this.nameLabel, 0);
+      this.Controls.SetChildIndex(this.FeatureCorrelationButton, 0);
+      ((System.ComponentModel.ISupportInitialize)(this.errorProvider)).EndInit();
+      this.ResumeLayout(false);
+      this.PerformLayout();
+
+    }
+
+    #endregion
+
+    protected System.Windows.Forms.Button FeatureCorrelationButton;
+
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/ProblemDataView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/ProblemDataView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/ProblemDataView.cs	(revision 8811)
@@ -0,0 +1,103 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Linq;
+using System.Text;
+using System.Windows.Forms;
+using HeuristicLab.Core;
+using HeuristicLab.Core.Views;
+using HeuristicLab.Data;
+using HeuristicLab.MainForm;
+using HeuristicLab.MainForm.WindowsForms;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Problem Data View")]
+  [Content(typeof(DataAnalysisProblemData), true)]
+  public partial class ProblemDataView : ParameterizedNamedItemView {
+
+    public new DataAnalysisProblemData Content {
+      get { return (DataAnalysisProblemData)base.Content; }
+      set { base.Content = value; }
+    }
+
+    public ProblemDataView() {
+      InitializeComponent();
+    }
+
+    protected void FeatureCorrelationButton_Click(object sender, System.EventArgs e) {
+      Type viewType = MainFormManager.GetViewTypes(this.Content.GetType(), true).FirstOrDefault(t => typeof(AbstractFeatureCorrelationView).IsAssignableFrom(t));
+      MainFormManager.MainForm.ShowContent(Content, viewType);
+    }
+
+    protected void parameterCollectionView_DragDrop(object sender, DragEventArgs e) {
+      if (e.Effect != DragDropEffects.None) {
+        var stringValueList = (e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat) as IFixedValueParameter).Value as ICheckedItemList<StringValue>;
+        SetInputVariables(stringValueList);
+      }
+    }
+
+    private void SetInputVariables(ICheckedItemList<StringValue> stringValueList) {
+      var inputVariables = Content.InputVariables;
+      var stringValues = stringValueList.Select(x => x.Value);
+      var notContainedVariables = inputVariables.Where(x => !stringValues.Contains(x.Value));
+
+      if (notContainedVariables.Count() != 0) {
+        StringBuilder strBuilder = new StringBuilder();
+        foreach (var variable in notContainedVariables) {
+          strBuilder.Append(variable.Value + ", ");
+        }
+        strBuilder.Remove(strBuilder.Length - 2, 2);
+        MessageBox.Show(String.Format("There was an error while changing the input variables. The following input " +
+          "variables have not been contained {0}", strBuilder.ToString()), "Error while changing the input variables",
+          MessageBoxButtons.OK, MessageBoxIcon.Warning);
+      } else {
+        var checkedItems = stringValueList.CheckedItems.Select(x => x.Value.Value);
+        var setChecked = inputVariables.Where(x => checkedItems.Contains(x.Value));
+        foreach (var variable in inputVariables) {
+          if (setChecked.Contains(variable) && !inputVariables.ItemChecked(variable)) {
+            inputVariables.SetItemCheckedState(variable, true);
+          } else if (!setChecked.Contains(variable) && inputVariables.ItemChecked(variable)) {
+            inputVariables.SetItemCheckedState(variable, false);
+          }
+        }
+      }
+    }
+
+    protected void parameterCollectionView_DragEnterOver(object sender, DragEventArgs e) {
+      e.Effect = DragDropEffects.None;
+      if (ReadOnly)
+        return;
+      if (e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat) == null)
+        return;
+
+      var parameter = e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat) as IFixedValueParameter;
+      if (parameter == null)
+        return;
+
+      var stringValueList = parameter.Value as ICheckedItemList<StringValue>;
+      if (stringValueList == null)
+        return;
+
+      e.Effect = e.AllowedEffect;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionFeatureCorrelationView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionFeatureCorrelationView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionFeatureCorrelationView.Designer.cs	(revision 8811)
@@ -0,0 +1,32 @@
+﻿namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class RegressionFeatureCorrelationView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      components = new System.ComponentModel.Container();
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+    }
+
+    #endregion
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionFeatureCorrelationView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionFeatureCorrelationView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionFeatureCorrelationView.cs	(revision 8811)
@@ -0,0 +1,46 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Linq;
+using HeuristicLab.MainForm;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Regression Feature Correlation View")]
+  [Content(typeof(RegressionProblemData), false)]
+  public partial class RegressionFeatureCorrelationView : FeatureCorrelationView {
+
+    public new RegressionProblemData Content {
+      get { return (RegressionProblemData)base.Content; }
+      set { base.Content = value; }
+    }
+
+    public RegressionFeatureCorrelationView() {
+      InitializeComponent();
+    }
+
+    protected override bool[] SetInitialVisibilityOfColumns() {
+      int i = Content.Dataset.DoubleVariables.ToList().FindIndex(x => x == Content.TargetVariable);
+      var initialVisibility = base.SetInitialVisibilityOfColumns();
+      initialVisibility[i] = true;
+      return initialVisibility;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionTimeframeFeatureCorrelationView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionTimeframeFeatureCorrelationView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionTimeframeFeatureCorrelationView.Designer.cs	(revision 8811)
@@ -0,0 +1,32 @@
+﻿namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class RegressionTimeframeFeatureCorrelationView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      components = new System.ComponentModel.Container();
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+    }
+
+    #endregion
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionTimeframeFeatureCorrelationView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionTimeframeFeatureCorrelationView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Regression/RegressionTimeframeFeatureCorrelationView.cs	(revision 8811)
@@ -0,0 +1,46 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Linq;
+using HeuristicLab.MainForm;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Regression Timeframe Feature Correlation View")]
+  [Content(typeof(RegressionProblemData), false)]
+  public partial class RegressionTimeframeFeatureCorrelationView : TimeframeFeatureCorrelationView {
+
+    public new RegressionProblemData Content {
+      get { return (RegressionProblemData)base.Content; }
+      set { base.Content = value; }
+    }
+
+    public RegressionTimeframeFeatureCorrelationView() {
+      InitializeComponent();
+    }
+
+    protected override bool[] SetInitialVisibilityOfColumns() {
+      int i = Content.Dataset.DoubleVariables.ToList().FindIndex(x => x == Content.TargetVariable);
+      var initialVisibility = base.SetInitialVisibilityOfColumns();
+      initialVisibility[i] = true;
+      return initialVisibility;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/ClassificationEnsembleSolutionView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/ClassificationEnsembleSolutionView.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/ClassificationEnsembleSolutionView.cs	(revision 8811)
@@ -26,5 +26,4 @@
 using HeuristicLab.MainForm;
 using HeuristicLab.PluginInfrastructure;
-using HeuristicLab.Problems.DataAnalysis.Interfaces;
 
 namespace HeuristicLab.Problems.DataAnalysis.Views {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/DataAnalysisSolutionView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/DataAnalysisSolutionView.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/DataAnalysisSolutionView.cs	(revision 8811)
@@ -147,4 +147,18 @@
     }
 
+    protected override void RebuildImageList() {
+      itemsListView.SmallImageList.Images.Clear();
+      foreach (ListViewItem listViewItem in itemsListView.Items) {
+        IResult result = listViewItem.Tag as IResult;
+        Type viewType = listViewItem.Tag as Type;
+        if (result != null) itemsListView.SmallImageList.Images.Add(result.ItemImage);
+        else if (viewType != null && typeof(IDataAnalysisSolutionEvaluationView).IsAssignableFrom(viewType))
+          itemsListView.SmallImageList.Images.Add(((IDataAnalysisSolutionEvaluationView)Activator.CreateInstance(viewType)).ViewImage);
+        else itemsListView.SmallImageList.Images.Add(HeuristicLab.Common.Resources.VSImageLibrary.Nothing);
+
+        listViewItem.ImageIndex = itemsListView.SmallImageList.Images.Count - 1;
+      }
+    }
+
     #region drag and drop
     protected override void itemsListView_DragEnter(object sender, DragEventArgs e) {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/NamedDataAnalysisSolutionView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/NamedDataAnalysisSolutionView.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/NamedDataAnalysisSolutionView.cs	(revision 8811)
@@ -53,7 +53,9 @@
         contentType = Content.GetType();
         panel.Controls.Clear();
-        var viewType = MainFormManager.GetViewTypes(Content.GetType(), true).Where(t => typeof(DataAnalysisSolutionView).IsAssignableFrom(t)).FirstOrDefault();
+        var viewType = MainFormManager.GetViewTypes(Content.GetType(), true).FirstOrDefault(t => typeof(DataAnalysisSolutionView).IsAssignableFrom(t));
         if (viewType != null) {
-          view = (DataAnalysisSolutionView)Activator.CreateInstance(viewType);
+          view = (DataAnalysisSolutionView)MainFormManager.CreateView(viewType);
+          view.Locked = Locked;
+          view.ReadOnly = ReadOnly;
           view.Dock = DockStyle.Fill;
           view.Content = Content;
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/TimeSeriesPrognosisSolutionView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/TimeSeriesPrognosisSolutionView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/TimeSeriesPrognosisSolutionView.Designer.cs	(revision 8811)
@@ -0,0 +1,73 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class TimeSeriesPrognosisSolutionView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      ((System.ComponentModel.ISupportInitialize)(this.splitContainer)).BeginInit();
+      this.splitContainer.Panel1.SuspendLayout();
+      this.splitContainer.Panel2.SuspendLayout();
+      this.splitContainer.SuspendLayout();
+      this.itemsGroupBox.SuspendLayout();
+      this.detailsGroupBox.SuspendLayout();
+      this.SuspendLayout();
+      // 
+      // itemsGroupBox
+      // 
+      this.itemsGroupBox.Text = "Time-series Prognosis Solution";
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Inherit;
+      this.Name = "RegressionSolutionView";
+      this.splitContainer.Panel1.ResumeLayout(false);
+      this.splitContainer.Panel2.ResumeLayout(false);
+      ((System.ComponentModel.ISupportInitialize)(this.splitContainer)).EndInit();
+      this.splitContainer.ResumeLayout(false);
+      this.itemsGroupBox.ResumeLayout(false);
+      this.detailsGroupBox.ResumeLayout(false);
+      this.ResumeLayout(false);
+
+    }
+
+    #endregion
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/TimeSeriesPrognosisSolutionView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/TimeSeriesPrognosisSolutionView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/Solution Views/TimeSeriesPrognosisSolutionView.cs	(revision 8811)
@@ -0,0 +1,53 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Windows.Forms;
+using HeuristicLab.Core;
+using HeuristicLab.MainForm;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("TimeSeriesPrognosisSolution View")]
+  [Content(typeof(TimeSeriesPrognosisSolution), false)]
+  public partial class TimeSeriesPrognosisSolutionView : RegressionSolutionView {
+    public TimeSeriesPrognosisSolutionView() {
+      InitializeComponent();
+    }
+
+    public new TimeSeriesPrognosisSolution Content {
+      get { return (TimeSeriesPrognosisSolution)base.Content; }
+      set { base.Content = value; }
+    }
+
+    #region drag and drop
+    protected override void itemsListView_DragEnter(object sender, DragEventArgs e) {
+      validDragOperation = false;
+      if (ReadOnly) return;
+
+      var dropData = e.Data.GetData(HeuristicLab.Common.Constants.DragDropDataFormat);
+      if (dropData is ITimeSeriesPrognosisProblemData) validDragOperation = true;
+      else if (dropData is IValueParameter) {
+        var param = (IValueParameter)dropData;
+        if (param.Value is ITimeSeriesPrognosisProblemData) validDragOperation = true;
+      }
+    }
+    #endregion
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisResultsView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisResultsView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisResultsView.Designer.cs	(revision 8811)
@@ -0,0 +1,169 @@
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class TimeSeriesPrognosisResultsView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing) {
+        if (components != null) components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.components = new System.ComponentModel.Container();
+      this.TrainingHorizonLabel = new System.Windows.Forms.Label();
+      this.TestHorizonLabel = new System.Windows.Forms.Label();
+      this.TrainingHorizonTextBox = new System.Windows.Forms.TextBox();
+      this.TestHorizonTextBox = new System.Windows.Forms.TextBox();
+      this.TrainingHorizonErrorProvider = new System.Windows.Forms.ErrorProvider(this.components);
+      this.TestHorizonErrorProvider = new System.Windows.Forms.ErrorProvider(this.components);
+      ((System.ComponentModel.ISupportInitialize)(this.splitContainer)).BeginInit();
+      this.splitContainer.Panel1.SuspendLayout();
+      this.splitContainer.Panel2.SuspendLayout();
+      this.splitContainer.SuspendLayout();
+      this.itemsGroupBox.SuspendLayout();
+      this.detailsGroupBox.SuspendLayout();
+      ((System.ComponentModel.ISupportInitialize)(this.TrainingHorizonErrorProvider)).BeginInit();
+      ((System.ComponentModel.ISupportInitialize)(this.TestHorizonErrorProvider)).BeginInit();
+      this.SuspendLayout();
+      // 
+      // splitContainer
+      // 
+      // 
+      // splitContainer.Panel1
+      // 
+      this.splitContainer.Panel1.Controls.Add(this.TestHorizonTextBox);
+      this.splitContainer.Panel1.Controls.Add(this.TrainingHorizonTextBox);
+      this.splitContainer.Panel1.Controls.Add(this.TrainingHorizonLabel);
+      this.splitContainer.Panel1.Controls.Add(this.TestHorizonLabel);
+      // 
+      // itemsListView
+      // 
+      this.itemsListView.Location = new System.Drawing.Point(3, 82);
+      this.itemsListView.Size = new System.Drawing.Size(244, 279);
+      // 
+      // addButton
+      // 
+      this.toolTip.SetToolTip(this.addButton, "Add");
+      // 
+      // removeButton
+      // 
+      this.toolTip.SetToolTip(this.removeButton, "Remove");
+      // 
+      // TrainingHorizonLabel
+      // 
+      this.TrainingHorizonLabel.AutoSize = true;
+      this.TrainingHorizonLabel.Location = new System.Drawing.Point(3, 34);
+      this.TrainingHorizonLabel.Name = "TrainingHorizonLabel";
+      this.TrainingHorizonLabel.Size = new System.Drawing.Size(87, 13);
+      this.TrainingHorizonLabel.TabIndex = 7;
+      this.TrainingHorizonLabel.Text = "Training Horizon:";
+      // 
+      // TestHorizonLabel
+      // 
+      this.TestHorizonLabel.AutoSize = true;
+      this.TestHorizonLabel.Location = new System.Drawing.Point(3, 60);
+      this.TestHorizonLabel.Name = "TestHorizonLabel";
+      this.TestHorizonLabel.Size = new System.Drawing.Size(73, 13);
+      this.TestHorizonLabel.TabIndex = 8;
+      this.TestHorizonLabel.Text = "Test Horizon: ";
+      // 
+      // TrainingHorizonTextBox
+      // 
+      this.TrainingHorizonTextBox.Anchor = ((System.Windows.Forms.AnchorStyles)(((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Left)
+            | System.Windows.Forms.AnchorStyles.Right)));
+      this.TrainingHorizonTextBox.Location = new System.Drawing.Point(96, 31);
+      this.TrainingHorizonTextBox.Name = "TrainingHorizonTextBox";
+      this.TrainingHorizonTextBox.Size = new System.Drawing.Size(151, 20);
+      this.TrainingHorizonTextBox.TabIndex = 1;
+      this.TrainingHorizonTextBox.KeyDown += new System.Windows.Forms.KeyEventHandler(this.TrainingHorizonTextBox_KeyDown);
+      this.TrainingHorizonTextBox.Validating += new System.ComponentModel.CancelEventHandler(this.TrainingHorizonTextBox_Validating);
+      this.TrainingHorizonTextBox.Validated += new System.EventHandler(this.TrainingHorizonTextBox_Validated);
+      // 
+      // TestHorizonTextBox
+      // 
+      this.TestHorizonTextBox.Anchor = ((System.Windows.Forms.AnchorStyles)(((System.Windows.Forms.AnchorStyles.Top | System.Windows.Forms.AnchorStyles.Left)
+            | System.Windows.Forms.AnchorStyles.Right)));
+      this.TestHorizonTextBox.Location = new System.Drawing.Point(96, 57);
+      this.TestHorizonTextBox.Name = "TestHorizonTextBox";
+      this.TestHorizonTextBox.Size = new System.Drawing.Size(151, 20);
+      this.TestHorizonTextBox.TabIndex = 1;
+      this.TestHorizonTextBox.KeyDown += new System.Windows.Forms.KeyEventHandler(this.TestHorizonTextBox_KeyDown);
+      this.TestHorizonTextBox.Validating += new System.ComponentModel.CancelEventHandler(this.TestHorizonTextBox_Validating);
+      this.TestHorizonTextBox.Validated += new System.EventHandler(this.TestHorizonTextBox_Validated);
+      // 
+      // TrainingHorizonErrorProvider
+      // 
+      this.TrainingHorizonErrorProvider.BlinkRate = 0;
+      this.TrainingHorizonErrorProvider.BlinkStyle = System.Windows.Forms.ErrorBlinkStyle.NeverBlink;
+      this.TrainingHorizonErrorProvider.ContainerControl = this;
+      // 
+      // TestHorizonErrorProvider
+      // 
+      this.TestHorizonErrorProvider.BlinkRate = 0;
+      this.TestHorizonErrorProvider.BlinkStyle = System.Windows.Forms.ErrorBlinkStyle.NeverBlink;
+      this.TestHorizonErrorProvider.ContainerControl = this;
+      // 
+      // TimeSeriesPrognosisResultsView
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+      this.Name = "TimeSeriesPrognosisResultsView";
+      this.splitContainer.Panel1.ResumeLayout(false);
+      this.splitContainer.Panel1.PerformLayout();
+      this.splitContainer.Panel2.ResumeLayout(false);
+      ((System.ComponentModel.ISupportInitialize)(this.splitContainer)).EndInit();
+      this.splitContainer.ResumeLayout(false);
+      this.itemsGroupBox.ResumeLayout(false);
+      this.detailsGroupBox.ResumeLayout(false);
+      ((System.ComponentModel.ISupportInitialize)(this.TrainingHorizonErrorProvider)).EndInit();
+      ((System.ComponentModel.ISupportInitialize)(this.TestHorizonErrorProvider)).EndInit();
+      this.ResumeLayout(false);
+
+    }
+
+    #endregion
+
+    private System.Windows.Forms.TextBox TestHorizonTextBox;
+    private System.Windows.Forms.TextBox TrainingHorizonTextBox;
+    private System.Windows.Forms.Label TrainingHorizonLabel;
+    private System.Windows.Forms.Label TestHorizonLabel;
+    private System.Windows.Forms.ErrorProvider TrainingHorizonErrorProvider;
+    private System.Windows.Forms.ErrorProvider TestHorizonErrorProvider;
+
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisResultsView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisResultsView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisResultsView.cs	(revision 8811)
@@ -0,0 +1,144 @@
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Windows.Forms;
+using HeuristicLab.MainForm;
+using HeuristicLab.Optimization.Views;
+
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("TimeSeriesPrognosisResultsView")]
+  [Content(typeof(TimeSeriesPrognosisResults), IsDefaultView = true)]
+  public partial class TimeSeriesPrognosisResultsView : ResultCollectionView {
+    public new TimeSeriesPrognosisResults Content {
+      get { return (TimeSeriesPrognosisResults)base.Content; }
+      set { base.Content = value; }
+    }
+
+    public override bool Locked {
+      get {
+        return base.Locked;
+      }
+      set {
+        base.Locked = value;
+      }
+    }
+
+    public TimeSeriesPrognosisResultsView() {
+      InitializeComponent();
+      TrainingHorizonErrorProvider.SetIconAlignment(TrainingHorizonTextBox, ErrorIconAlignment.MiddleLeft);
+      TrainingHorizonErrorProvider.SetIconPadding(TrainingHorizonTextBox, 2);
+      TestHorizonErrorProvider.SetIconAlignment(TestHorizonTextBox, ErrorIconAlignment.MiddleLeft);
+      TestHorizonErrorProvider.SetIconPadding(TestHorizonTextBox, 2);
+    }
+
+    #region Content Events
+    protected override void RegisterContentEvents() {
+      base.RegisterContentEvents();
+      Content.TrainingHorizonChanged += new System.EventHandler(Content_TrainingHorizonChanged);
+      Content.TestHorizonChanged += new System.EventHandler(Content_TestHorizonChanged);
+    }
+    protected override void DeregisterContentEvents() {
+      Content.TrainingHorizonChanged -= new System.EventHandler(Content_TrainingHorizonChanged);
+      Content.TestHorizonChanged -= new System.EventHandler(Content_TestHorizonChanged);
+      base.DeregisterContentEvents();
+    }
+
+    private void Content_TrainingHorizonChanged(object sender, System.EventArgs e) {
+      TrainingHorizonTextBox.Text = Content.TrainingHorizon.ToString();
+    }
+
+    private void Content_TestHorizonChanged(object sender, System.EventArgs e) {
+      TestHorizonTextBox.Text = Content.TestHorizon.ToString();
+    }
+    #endregion
+
+    protected override void OnContentChanged() {
+      base.OnContentChanged();
+      if (Content == null) {
+        TrainingHorizonTextBox.Text = string.Empty;
+        TestHorizonTextBox.Text = string.Empty;
+      } else {
+        TrainingHorizonTextBox.Text = Content.TrainingHorizon.ToString();
+        TestHorizonTextBox.Text = Content.TestHorizon.ToString();
+      }
+    }
+
+    protected override void SetEnabledStateOfControls() {
+      base.SetEnabledStateOfControls();
+      //necessary cause this could be triggered by the base ctor when the controls are not created yet
+      if (TrainingHorizonTextBox == null || TestHorizonTextBox == null) return;
+      TrainingHorizonTextBox.Enabled = Content != null && !Locked;
+      TestHorizonTextBox.Enabled = Content != null && !Locked;
+    }
+
+    #region Control events
+    private void TrainingHorizonTextBox_Validating(object sender, System.ComponentModel.CancelEventArgs e) {
+      int trainingHorizon;
+      if (!int.TryParse(TrainingHorizonTextBox.Text, out trainingHorizon)) {
+        e.Cancel = true;
+        TrainingHorizonErrorProvider.SetError(TrainingHorizonTextBox, "Please enter a numeric value.");
+        TrainingHorizonTextBox.SelectAll();
+      }
+    }
+
+    private void TrainingHorizonTextBox_Validated(object sender, System.EventArgs e) {
+      Content.TrainingHorizon = int.Parse(TrainingHorizonTextBox.Text);
+      TrainingHorizonErrorProvider.SetError(TrainingHorizonTextBox, string.Empty);
+      TrainingHorizonTextBox.Text = Content.TrainingHorizon.ToString();
+    }
+
+    private void TrainingHorizonTextBox_KeyDown(object sender, KeyEventArgs e) {
+      if ((e.KeyCode == Keys.Enter) || (e.KeyCode == Keys.Return))
+        TrainingHorizonLabel.Select();  // select label to validate data
+
+      if (e.KeyCode == Keys.Escape) {
+        TrainingHorizonTextBox.Text = Content.TrainingHorizon.ToString();
+        TrainingHorizonLabel.Select();  // select label to validate data
+      }
+    }
+
+    private void TestHorizonTextBox_Validating(object sender, System.ComponentModel.CancelEventArgs e) {
+      int testHorizon;
+      if (!int.TryParse(TestHorizonTextBox.Text, out testHorizon)) {
+        e.Cancel = true;
+        TestHorizonErrorProvider.SetError(TestHorizonTextBox, "Please enter a numeric value.");
+        TestHorizonTextBox.SelectAll();
+      }
+    }
+
+    private void TestHorizonTextBox_Validated(object sender, System.EventArgs e) {
+      Content.TestHorizon = int.Parse(TestHorizonTextBox.Text);
+      TestHorizonErrorProvider.SetError(TestHorizonTextBox, string.Empty);
+      TestHorizonTextBox.Text = Content.TestHorizon.ToString();
+    }
+
+    private void TestHorizonTextBox_KeyDown(object sender, KeyEventArgs e) {
+      if ((e.KeyCode == Keys.Enter) || (e.KeyCode == Keys.Return))
+        TestHorizonLabel.Select();  // select label to validate data
+
+      if (e.KeyCode == Keys.Escape) {
+        TestHorizonTextBox.Text = Content.TestHorizon.ToString();
+        TestHorizonLabel.Select();  // select label to validate data
+      }
+    }
+    #endregion
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.Designer.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.Designer.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.Designer.cs	(revision 8811)
@@ -0,0 +1,43 @@
+﻿namespace HeuristicLab.Problems.DataAnalysis.Views {
+  partial class TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView {
+    /// <summary> 
+    /// Required designer variable.
+    /// </summary>
+    private System.ComponentModel.IContainer components = null;
+
+    /// <summary> 
+    /// Clean up any resources being used.
+    /// </summary>
+    /// <param name="disposing">true if managed resources should be disposed; otherwise, false.</param>
+    protected override void Dispose(bool disposing) {
+      if (disposing && (components != null)) {
+        components.Dispose();
+      }
+      base.Dispose(disposing);
+    }
+
+    #region Component Designer generated code
+
+    /// <summary> 
+    /// Required method for Designer support - do not modify 
+    /// the contents of this method with the code editor.
+    /// </summary>
+    private void InitializeComponent() {
+      this.components = new System.ComponentModel.Container();
+      ((System.ComponentModel.ISupportInitialize)(this.chart)).BeginInit();
+      this.SuspendLayout();
+      // 
+      // TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView
+      // 
+      this.AutoScaleDimensions = new System.Drawing.SizeF(6F, 13F);
+      this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font;
+      this.Name = "TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView";
+      ((System.ComponentModel.ISupportInitialize)(this.chart)).EndInit();
+      this.ResumeLayout(false);
+      this.PerformLayout();
+
+    }
+
+    #endregion
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis.Views/3.4/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView.cs	(revision 8811)
@@ -0,0 +1,63 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+using System.Linq;
+using System.Windows.Forms;
+using HeuristicLab.MainForm;
+using HeuristicLab.MainForm.WindowsForms;
+namespace HeuristicLab.Problems.DataAnalysis.Views {
+  [View("Error Characteristics Curve")]
+  [Content(typeof(ITimeSeriesPrognosisSolution))]
+  public partial class TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView : RegressionSolutionErrorCharacteristicsCurveView {
+
+
+    public TimeSeriesPrognosisSolutionErrorCharacteristicsCurveView()
+      : base() {
+      InitializeComponent();
+    }
+
+    public new ITimeSeriesPrognosisSolution Content {
+      get { return (ITimeSeriesPrognosisSolution)base.Content; }
+      set { base.Content = value; }
+    }
+    public new ITimeSeriesPrognosisProblemData ProblemData {
+      get {
+        if (Content == null) return null;
+        return Content.ProblemData;
+      }
+    }
+
+    protected override void UpdateChart() {
+      base.UpdateChart();
+      if (Content == null) return;
+
+      //AR1 model
+      double alpha, beta;
+      OnlineCalculatorError errorState;
+      IEnumerable<double> trainingStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
+      OnlineLinearScalingParameterCalculator.Calculate(ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
+      var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(ProblemData.TargetVariable, new double[] { beta }, alpha).CreateTimeSeriesPrognosisSolution(ProblemData);
+      AR1model.Name = "AR(1) Model";
+      AddRegressionSolution(AR1model);
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Dataset.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Dataset.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Dataset.cs	(revision 8811)
@@ -193,6 +193,5 @@
       if (values == null) throw new ArgumentException("The varialbe " + variableName + " is not a double variable.");
 
-      foreach (int index in rows)
-        yield return values[index];
+      return rows.Select(index => values[index]);
     }
 
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/DatasetExtensions.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/DatasetExtensions.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/DatasetExtensions.cs	(revision 8811)
@@ -0,0 +1,34 @@
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public static class DatasetExtensions {
+    public static IEnumerable<T> TakeEvery<T>(this IEnumerable<T> xs, int nth) {
+      int i = 0;
+      foreach (var x in xs) {
+        if (i % nth == 0) yield return x;
+        i++;
+      }
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/HeuristicLab.Problems.DataAnalysis-3.4.csproj
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/HeuristicLab.Problems.DataAnalysis-3.4.csproj	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/HeuristicLab.Problems.DataAnalysis-3.4.csproj	(revision 8811)
@@ -93,4 +93,12 @@
   </PropertyGroup>
   <ItemGroup>
+    <Reference Include="ALGLIB-3.6.0, Version=3.6.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
+      <HintPath>>..\..\..\..\trunk\sources\bin\ALGLIB-3.6.0.dll</HintPath>
+      <Private>False</Private>
+    </Reference>
+    <Reference Include="HeuristicLab.ALGLIB-3.6.0, Version=3.6.0.0, Culture=neutral, PublicKeyToken=ba48961d6f65dcec, processorArchitecture=MSIL">
+      <HintPath>>..\..\..\..\trunk\sources\bin\HeuristicLab.ALGLIB-3.6.0.dll</HintPath>
+      <Private>False</Private>
+    </Reference>
     <Reference Include="HeuristicLab.Collections-3.3">
       <HintPath>..\..\..\..\trunk\sources\bin\HeuristicLab.Collections-3.3.dll</HintPath>
@@ -147,4 +155,5 @@
   </ItemGroup>
   <ItemGroup>
+    <Compile Include="DatasetExtensions.cs" />
     <Compile Include="DoubleLimit.cs" />
     <Compile Include="Implementation\Classification\ClassificationEnsembleModel.cs">
@@ -175,4 +184,11 @@
     </Compile>
     <Compile Include="Implementation\Regression\RegressionEnsembleSolution.cs" />
+    <Compile Include="Implementation\TimeSeriesPrognosis\Models\ConstantTimeSeriesPrognosisModel.cs" />
+    <Compile Include="Implementation\TimeSeriesPrognosis\Models\TimeSeriesPrognosisAutoRegressiveModel.cs" />
+    <Compile Include="Implementation\TimeSeriesPrognosis\TimeSeriesPrognosisProblem.cs" />
+    <Compile Include="Implementation\TimeSeriesPrognosis\TimeSeriesPrognosisProblemData.cs" />
+    <Compile Include="Implementation\TimeSeriesPrognosis\TimeSeriesPrognosisResults.cs" />
+    <Compile Include="Implementation\TimeSeriesPrognosis\TimeSeriesPrognosisSolution.cs" />
+    <Compile Include="Implementation\TimeSeriesPrognosis\TimeSeriesPrognosisSolutionBase.cs" />
     <Compile Include="Interfaces\Classification\IClassificationEnsembleModel.cs">
       <SubType>Code</SubType>
@@ -188,5 +204,13 @@
     <Compile Include="Interfaces\Regression\IRegressionEnsembleSolution.cs" />
     <Compile Include="Implementation\Regression\RegressionSolutionBase.cs" />
+    <Compile Include="Interfaces\TimeSeriesPrognosis\IOnlineTimeSeriesCalculator.cs" />
+    <Compile Include="Interfaces\TimeSeriesPrognosis\ITimeSeriesPrognosisModel.cs" />
+    <Compile Include="Interfaces\TimeSeriesPrognosis\ITimeSeriesPrognosisProblem.cs" />
+    <Compile Include="Interfaces\TimeSeriesPrognosis\ITimeSeriesPrognosisProblemData.cs" />
+    <Compile Include="Interfaces\TimeSeriesPrognosis\ITimeSeriesPrognosisSolution.cs" />
+    <Compile Include="OnlineCalculators\AutoCorrelationCalculator.cs" />
+    <Compile Include="OnlineCalculators\OnlineBoundedMeanSquaredErrorCalculator.cs" />
     <Compile Include="OnlineCalculators\HoeffdingsDependenceCalculator.cs" />
+    <Compile Include="OnlineCalculators\OnlineDirectionalSymmetryCalculator.cs" />
     <Compile Include="OnlineCalculators\OnlineMaxAbsoluteErrorCalculator.cs" />
     <Compile Include="OnlineCalculators\OnlineMeanErrorCalculator.cs" />
@@ -227,4 +251,7 @@
     <Compile Include="OnlineCalculators\OnlinePearsonsRSquaredCalculator.cs" />
     <Compile Include="Implementation\Regression\RegressionSolution.cs" />
+    <Compile Include="OnlineCalculators\OnlineTheilsUStatisticCalculator.cs" />
+    <Compile Include="OnlineCalculators\OnlineWeightedDirectionalSymmetryCalculator.cs" />
+    <Compile Include="OnlineCalculators\SpearmansRankCorrelationCoefficientCalculator.cs" />
     <Compile Include="Plugin.cs" />
     <Compile Include="Implementation\Classification\ThresholdCalculators\AccuracyMaximizationThresholdCalculator.cs" />
@@ -256,4 +283,5 @@
   </ItemGroup>
   <ItemGroup />
+  <ItemGroup />
   <Import Project="$(MSBuildToolsPath)\Microsoft.CSharp.targets" />
   <!-- To modify your build process, add your task inside one of the targets below and uncomment it. 
@@ -265,5 +293,5 @@
   -->
   <PropertyGroup>
-    <PreBuildEvent>set Path=%25Path%25;$(ProjectDir);$(SolutionDir)
+    <PreBuildEvent Condition=" '$(OS)' == 'Windows_NT' ">set Path=%25Path%25;$(ProjectDir);$(SolutionDir)
 set ProjectDir=$(ProjectDir)
 set SolutionDir=$(SolutionDir)
@@ -272,4 +300,10 @@
 call PreBuildEvent.cmd
 </PreBuildEvent>
+    <PreBuildEvent Condition=" '$(OS)' != 'Windows_NT' ">
+export ProjectDir=$(ProjectDir)
+export SolutionDir=$(SolutionDir)
+
+$SolutionDir/PreBuildEvent.sh
+</PreBuildEvent>
   </PropertyGroup>
 </Project>
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs	(revision 8811)
@@ -28,5 +28,4 @@
 using HeuristicLab.Data;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.Problems.DataAnalysis.Interfaces;
 
 namespace HeuristicLab.Problems.DataAnalysis {
@@ -37,5 +36,9 @@
   [Item("Classification Ensemble Solution", "A classification solution that contains an ensemble of multiple classification models")]
   [Creatable("Data Analysis - Ensembles")]
-  public sealed class ClassificationEnsembleSolution : ClassificationSolution, IClassificationEnsembleSolution {
+  public sealed class ClassificationEnsembleSolution : ClassificationSolutionBase, IClassificationEnsembleSolution {
+    private readonly Dictionary<int, double> trainingEvaluationCache = new Dictionary<int, double>();
+    private readonly Dictionary<int, double> testEvaluationCache = new Dictionary<int, double>();
+    private readonly Dictionary<int, double> evaluationCache = new Dictionary<int, double>();
+
     public new IClassificationEnsembleModel Model {
       get { return (IClassificationEnsembleModel)base.Model; }
@@ -162,10 +165,10 @@
     }
 
-    protected override void RecalculateResults() {
-      weightCalculator.CalculateNormalizedWeights(classificationSolutions.CheckedItems);
-      CalculateResults();
-    }
 
     #region Evaluation
+    public override IEnumerable<double> EstimatedClassValues {
+      get { return GetEstimatedClassValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
+    }
+
     public override IEnumerable<double> EstimatedTrainingClassValues {
       get {
@@ -258,4 +261,9 @@
     }
 
+    protected override void RecalculateResults() {
+      weightCalculator.CalculateNormalizedWeights(classificationSolutions.CheckedItems);
+      base.RecalculateResults();
+    }
+
     private void AddClassificationSolution(IClassificationSolution solution) {
       if (Model.Models.Contains(solution.Model)) throw new ArgumentException();
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationProblemData.cs	(revision 8811)
@@ -223,38 +223,34 @@
     }
 
-    private List<double> classValues;
-    public List<double> ClassValues {
+    private List<double> classValuesCache;
+    private List<double> ClassValuesCache {
       get {
-        if (classValues == null) {
-          classValues = Dataset.GetDoubleValues(TargetVariableParameter.Value.Value).Distinct().ToList();
-          classValues.Sort();
+        if (classValuesCache == null) {
+          classValuesCache = Dataset.GetDoubleValues(TargetVariableParameter.Value.Value).Distinct().OrderBy(x => x).ToList();
         }
-        return classValues;
+        return classValuesCache;
       }
     }
-    IEnumerable<double> IClassificationProblemData.ClassValues {
-      get { return ClassValues; }
-    }
-
+    public IEnumerable<double> ClassValues {
+      get { return ClassValuesCache; }
+    }
     public int Classes {
-      get { return ClassValues.Count; }
-    }
-
-    private List<string> classNames;
-    public List<string> ClassNames {
+      get { return ClassValuesCache.Count; }
+    }
+
+    private List<string> classNamesCache;
+    private List<string> ClassNamesCache {
       get {
-        if (classNames == null) {
-          classNames = new List<string>();
+        if (classNamesCache == null) {
+          classNamesCache = new List<string>();
           for (int i = 0; i < ClassNamesParameter.Value.Rows; i++)
-            classNames.Add(ClassNamesParameter.Value[i, 0]);
+            classNamesCache.Add(ClassNamesParameter.Value[i, 0]);
         }
-        return classNames;
+        return classNamesCache;
       }
     }
-    IEnumerable<string> IClassificationProblemData.ClassNames {
-      get { return ClassNames; }
-    }
-
-    private Dictionary<Tuple<double, double>, double> classificationPenaltiesCache = new Dictionary<Tuple<double, double>, double>();
+    public IEnumerable<string> ClassNames {
+      get { return ClassNamesCache; }
+    }
     #endregion
 
@@ -284,4 +280,13 @@
       TestPartition.Start = classificationProblemData.TestPartition.Start;
       TestPartition.End = classificationProblemData.TestPartition.End;
+
+      for (int i = 0; i < classificationProblemData.ClassNames.Count(); i++)
+        ClassNamesParameter.Value[i, 0] = classificationProblemData.ClassNames.ElementAt(i);
+
+      for (int i = 0; i < Classes; i++) {
+        for (int j = 0; j < Classes; j++) {
+          ClassificationPenaltiesParameter.Value[i, j] = classificationProblemData.GetClassificationPenalty(ClassValuesCache[i], ClassValuesCache[j]);
+        }
+      }
     }
 
@@ -295,6 +300,6 @@
       Parameters.Add(new FixedValueParameter<DoubleMatrix>(ClassificationPenaltiesParameterName, ""));
 
+      RegisterParameterEvents();
       ResetTargetVariableDependentMembers();
-      RegisterParameterEvents();
     }
 
@@ -319,14 +324,11 @@
       DeregisterParameterEvents();
 
-      classNames = null;
       ((IStringConvertibleMatrix)ClassNamesParameter.Value).Columns = 1;
-      ((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = ClassValues.Count;
+      ((IStringConvertibleMatrix)ClassNamesParameter.Value).Rows = ClassValuesCache.Count;
       for (int i = 0; i < Classes; i++)
-        ClassNamesParameter.Value[i, 0] = "Class " + ClassValues[i];
+        ClassNamesParameter.Value[i, 0] = "Class " + ClassValuesCache[i];
       ClassNamesParameter.Value.ColumnNames = new List<string>() { "ClassNames" };
       ClassNamesParameter.Value.RowNames = ClassValues.Select(s => "ClassValue: " + s);
 
-      classificationPenaltiesCache.Clear();
-      ((ValueParameter<DoubleMatrix>)ClassificationPenaltiesParameter).ReactOnValueToStringChangedAndValueItemImageChanged = false;
       ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Rows = Classes;
       ((IStringConvertibleMatrix)ClassificationPenaltiesParameter.Value).Columns = Classes;
@@ -339,23 +341,22 @@
         }
       }
-      ((ValueParameter<DoubleMatrix>)ClassificationPenaltiesParameter).ReactOnValueToStringChangedAndValueItemImageChanged = true;
       RegisterParameterEvents();
     }
 
     public string GetClassName(double classValue) {
-      if (!ClassValues.Contains(classValue)) throw new ArgumentException();
-      int index = ClassValues.IndexOf(classValue);
-      return ClassNames[index];
+      if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
+      int index = ClassValuesCache.IndexOf(classValue);
+      return ClassNamesCache[index];
     }
     public double GetClassValue(string className) {
-      if (!ClassNames.Contains(className)) throw new ArgumentException();
-      int index = ClassNames.IndexOf(className);
-      return ClassValues[index];
+      if (!ClassNamesCache.Contains(className)) throw new ArgumentException();
+      int index = ClassNamesCache.IndexOf(className);
+      return ClassValuesCache[index];
     }
     public void SetClassName(double classValue, string className) {
-      if (!classValues.Contains(classValue)) throw new ArgumentException();
-      int index = ClassValues.IndexOf(classValue);
-      ClassNames[index] = className;
+      if (!ClassValuesCache.Contains(classValue)) throw new ArgumentException();
+      int index = ClassValuesCache.IndexOf(classValue);
       ClassNamesParameter.Value[index, 0] = className;
+      // updating of class names cache is not necessary here as the parameter value fires a changed event which updates the cache
     }
 
@@ -364,11 +365,7 @@
     }
     public double GetClassificationPenalty(double correctClassValue, double estimatedClassValue) {
-      var key = Tuple.Create(correctClassValue, estimatedClassValue);
-      if (!classificationPenaltiesCache.ContainsKey(key)) {
-        int correctClassIndex = ClassValues.IndexOf(correctClassValue);
-        int estimatedClassIndex = ClassValues.IndexOf(estimatedClassValue);
-        classificationPenaltiesCache[key] = ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex];
-      }
-      return classificationPenaltiesCache[key];
+      int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
+      int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
+      return ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex];
     }
     public void SetClassificationPenalty(string correctClassName, string estimatedClassName, double penalty) {
@@ -376,7 +373,6 @@
     }
     public void SetClassificationPenalty(double correctClassValue, double estimatedClassValue, double penalty) {
-      var key = Tuple.Create(correctClassValue, estimatedClassValue);
-      int correctClassIndex = ClassValues.IndexOf(correctClassValue);
-      int estimatedClassIndex = ClassValues.IndexOf(estimatedClassValue);
+      int correctClassIndex = ClassValuesCache.IndexOf(correctClassValue);
+      int estimatedClassIndex = ClassValuesCache.IndexOf(estimatedClassValue);
 
       ClassificationPenaltiesParameter.Value[correctClassIndex, estimatedClassIndex] = penalty;
@@ -387,25 +383,26 @@
       TargetVariableParameter.ValueChanged += new EventHandler(TargetVariableParameter_ValueChanged);
       ClassNamesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
-      ClassNamesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(MatrixParameter_ItemChanged);
+      ClassNamesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
+      ClassificationPenaltiesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
       ClassificationPenaltiesParameter.Value.Reset += new EventHandler(Parameter_ValueChanged);
-      ClassificationPenaltiesParameter.Value.ItemChanged += new EventHandler<EventArgs<int, int>>(MatrixParameter_ItemChanged);
     }
     private void DeregisterParameterEvents() {
       TargetVariableParameter.ValueChanged -= new EventHandler(TargetVariableParameter_ValueChanged);
       ClassNamesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
-      ClassNamesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(MatrixParameter_ItemChanged);
+      ClassNamesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
+      ClassificationPenaltiesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(Parameter_ValueChanged);
       ClassificationPenaltiesParameter.Value.Reset -= new EventHandler(Parameter_ValueChanged);
-      ClassificationPenaltiesParameter.Value.ItemChanged -= new EventHandler<EventArgs<int, int>>(MatrixParameter_ItemChanged);
     }
 
     private void TargetVariableParameter_ValueChanged(object sender, EventArgs e) {
-      classValues = null;
+      classValuesCache = null;
+      classNamesCache = null;
       ResetTargetVariableDependentMembers();
       OnChanged();
     }
     private void Parameter_ValueChanged(object sender, EventArgs e) {
-      OnChanged();
-    }
-    private void MatrixParameter_ItemChanged(object sender, EventArgs<int, int> e) {
+      classNamesCache = null;
+      ClassificationPenaltiesParameter.Value.RowNames = ClassNames.Select(name => "Actual " + name);
+      ClassificationPenaltiesParameter.Value.ColumnNames = ClassNames.Select(name => "Estimated " + name);
       OnChanged();
     }
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolution.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolution.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolution.cs	(revision 8811)
@@ -45,4 +45,5 @@
       : base(model, problemData) {
       evaluationCache = new Dictionary<int, double>(problemData.Dataset.Rows);
+      CalculateClassificationResults();
     }
 
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolutionBase.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolutionBase.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationSolutionBase.cs	(revision 8811)
@@ -85,5 +85,5 @@
     }
 
-    protected void CalculateResults() {
+    protected void CalculateClassificationResults() {
       double[] estimatedTrainingClassValues = EstimatedTrainingClassValues.ToArray(); // cache values
       double[] originalTrainingClassValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToArray();
@@ -114,4 +114,8 @@
 
     public abstract IEnumerable<double> GetEstimatedClassValues(IEnumerable<int> rows);
+
+    protected override void RecalculateResults() {
+      CalculateClassificationResults();
+    }
   }
 }
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationModel.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationModel.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationModel.cs	(revision 8811)
@@ -33,7 +33,11 @@
   [StorableClass]
   [Item("DiscriminantFunctionClassificationModel", "Represents a classification model that uses a discriminant function and classification thresholds.")]
-  public abstract class DiscriminantFunctionClassificationModel : NamedItem, IDiscriminantFunctionClassificationModel {
+  public class DiscriminantFunctionClassificationModel : NamedItem, IDiscriminantFunctionClassificationModel {
     [Storable]
     private IRegressionModel model;
+    public IRegressionModel Model {
+      get { return model; }
+      private set { model = value; }
+    }
 
     [Storable]
@@ -51,4 +55,11 @@
     }
 
+    private IDiscriminantFunctionThresholdCalculator thresholdCalculator;
+    [Storable]
+    public IDiscriminantFunctionThresholdCalculator ThresholdCalculator {
+      get { return thresholdCalculator; }
+      private set { thresholdCalculator = value; }
+    }
+
 
     [StorableConstructor]
@@ -61,11 +72,21 @@
     }
 
-    public DiscriminantFunctionClassificationModel(IRegressionModel model)
+    public DiscriminantFunctionClassificationModel(IRegressionModel model, IDiscriminantFunctionThresholdCalculator thresholdCalculator)
       : base() {
       this.name = ItemName;
       this.description = ItemDescription;
       this.model = model;
-      this.classValues = new double[] { 0.0 };
-      this.thresholds = new double[] { double.NegativeInfinity };
+      this.classValues = new double[0];
+      this.thresholds = new double[0];
+      this.thresholdCalculator = thresholdCalculator;
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      if (ThresholdCalculator == null) ThresholdCalculator = new AccuracyMaximizationThresholdCalculator();
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new DiscriminantFunctionClassificationModel(this, cloner);
     }
 
@@ -80,4 +101,14 @@
     }
 
+    public virtual void RecalculateModelParameters(IClassificationProblemData problemData, IEnumerable<int> rows) {
+      double[] classValues;
+      double[] thresholds;
+      var targetClassValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, rows);
+      var estimatedTrainingValues = GetEstimatedValues(problemData.Dataset, rows);
+      thresholdCalculator.Calculate(problemData, estimatedTrainingValues, targetClassValues, out classValues, out thresholds);
+      SetThresholdsAndClassValues(thresholds, classValues);
+    }
+
+
     public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
       return model.GetEstimatedValues(dataset, rows);
@@ -85,4 +116,5 @@
 
     public IEnumerable<double> GetEstimatedClassValues(Dataset dataset, IEnumerable<int> rows) {
+      if (!Thresholds.Any() && !ClassValues.Any()) throw new ArgumentException("No thresholds and class values were set for the current classification model.");
       foreach (var x in GetEstimatedValues(dataset, rows)) {
         int classIndex = 0;
@@ -103,6 +135,11 @@
     #endregion
 
-    public abstract IDiscriminantFunctionClassificationSolution CreateDiscriminantFunctionClassificationSolution(IClassificationProblemData problemData);
-    public abstract IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData);
+    public virtual IDiscriminantFunctionClassificationSolution CreateDiscriminantFunctionClassificationSolution(IClassificationProblemData problemData) {
+      return new DiscriminantFunctionClassificationSolution(this, problemData);
+    }
+
+    public virtual IClassificationSolution CreateClassificationSolution(IClassificationProblemData problemData) {
+      return CreateDiscriminantFunctionClassificationSolution(problemData);
+    }
   }
 }
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolution.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolution.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolution.cs	(revision 8811)
@@ -32,5 +32,5 @@
   [StorableClass]
   [Item("DiscriminantFunctionClassificationSolution", "Represents a classification solution that uses a discriminant function and classification thresholds.")]
-  public abstract class DiscriminantFunctionClassificationSolution : DiscriminantFunctionClassificationSolutionBase {
+  public class DiscriminantFunctionClassificationSolution : DiscriminantFunctionClassificationSolutionBase {
     protected readonly Dictionary<int, double> valueEvaluationCache;
     protected readonly Dictionary<int, double> classValueEvaluationCache;
@@ -47,8 +47,14 @@
       classValueEvaluationCache = new Dictionary<int, double>(original.classValueEvaluationCache);
     }
-    protected DiscriminantFunctionClassificationSolution(IDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData)
+    public DiscriminantFunctionClassificationSolution(IDiscriminantFunctionClassificationModel model, IClassificationProblemData problemData)
       : base(model, problemData) {
       valueEvaluationCache = new Dictionary<int, double>();
       classValueEvaluationCache = new Dictionary<int, double>();
+      CalculateRegressionResults();
+      CalculateClassificationResults();
+    }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new DiscriminantFunctionClassificationSolution(this, cloner);
     }
 
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/DiscriminantFunctionClassificationSolutionBase.cs	(revision 8811)
@@ -85,5 +85,4 @@
       Add(new Result(TrainingRSquaredResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition", new DoubleValue()));
       Add(new Result(TestRSquaredResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition", new DoubleValue()));
-
       RegisterEventHandler();
     }
@@ -92,10 +91,4 @@
     private void AfterDeserialization() {
       RegisterEventHandler();
-    }
-
-    protected override void OnModelChanged() {
-      DeregisterEventHandler();
-      RegisterEventHandler();
-      base.OnModelChanged();
     }
 
@@ -137,6 +130,5 @@
 
     protected virtual void OnModelThresholdsChanged(EventArgs e) {
-      CalculateResults();
-      CalculateRegressionResults();
+      OnModelChanged();
     }
 
@@ -146,4 +138,9 @@
 
     public abstract IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows);
+
+    protected override void RecalculateResults() {
+      base.RecalculateResults();
+      CalculateRegressionResults();
+    }
   }
 }
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/AccuracyMaximizationThresholdCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/AccuracyMaximizationThresholdCalculator.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/AccuracyMaximizationThresholdCalculator.cs	(revision 8811)
@@ -53,6 +53,6 @@
 
     public static void CalculateThresholds(IClassificationProblemData problemData, IEnumerable<double> estimatedValues, IEnumerable<double> targetClassValues, out double[] classValues, out double[] thresholds) {
-      int slices = 100;
-      double minThresholdInc = 10e-5; // necessary to prevent infinite loop when maxEstimated - minEstimated is effectively zero (constant model)
+      const int slices = 100;
+      const double minThresholdInc = 10e-5; // necessary to prevent infinite loop when maxEstimated - minEstimated is effectively zero (constant model)
       List<double> estimatedValuesList = estimatedValues.ToList();
       double maxEstimatedValue = estimatedValuesList.Max();
@@ -61,12 +61,13 @@
       var estimatedAndTargetValuePairs =
         estimatedValuesList.Zip(targetClassValues, (x, y) => new { EstimatedValue = x, TargetClassValue = y })
-        .OrderBy(x => x.EstimatedValue)
-        .ToList();
+        .OrderBy(x => x.EstimatedValue).ToList();
 
-      classValues = problemData.ClassValues.OrderBy(x => x).ToArray();
+      classValues = estimatedAndTargetValuePairs.GroupBy(x => x.TargetClassValue)
+        .Select(x => new { Median = x.Select(y => y.EstimatedValue).Median(), Class = x.Key })
+        .OrderBy(x => x.Median).Select(x => x.Class).ToArray();
+
       int nClasses = classValues.Length;
       thresholds = new double[nClasses];
       thresholds[0] = double.NegativeInfinity;
-      // thresholds[thresholds.Length - 1] = double.PositiveInfinity;
 
       // incrementally calculate accuracy of all possible thresholds
@@ -85,19 +86,24 @@
             //all positives
             if (pair.TargetClassValue.IsAlmost(classValues[i - 1])) {
-              if (pair.EstimatedValue > lowerThreshold && pair.EstimatedValue < actualThreshold)
+              if (pair.EstimatedValue > lowerThreshold && pair.EstimatedValue <= actualThreshold)
                 //true positive
-                classificationScore += problemData.GetClassificationPenalty(classValues[i - 1], classValues[i - 1]);
+                classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, pair.TargetClassValue);
               else
                 //false negative
-                classificationScore += problemData.GetClassificationPenalty(classValues[i], classValues[i - 1]);
+                classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, classValues[i]);
             }
               //all negatives
             else {
-              if (pair.EstimatedValue > lowerThreshold && pair.EstimatedValue < actualThreshold)
-                //false positive
-                classificationScore += problemData.GetClassificationPenalty(classValues[i - 1], classValues[i]);
-              else
-                //true negative, consider only upper class
-                classificationScore += problemData.GetClassificationPenalty(classValues[i], classValues[i]);
+              //false positive
+              if (pair.EstimatedValue > lowerThreshold && pair.EstimatedValue <= actualThreshold)
+                classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, classValues[i - 1]);
+              else if (pair.EstimatedValue <= lowerThreshold)
+                classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, classValues[i - 2]);
+              else if (pair.EstimatedValue > actualThreshold) {
+                if (pair.TargetClassValue < classValues[i - 1]) //negative in wrong class, consider upper class
+                  classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, classValues[i]);
+                else //true negative, must be optimized by the other thresholds
+                  classificationScore += problemData.GetClassificationPenalty(pair.TargetClassValue, pair.TargetClassValue);
+              }
             }
           }
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ThresholdCalculators/NormalDistributionCutPointsThresholdCalculator.cs	(revision 8811)
@@ -53,7 +53,6 @@
 
     public static void CalculateThresholds(IClassificationProblemData problemData, IEnumerable<double> estimatedValues, IEnumerable<double> targetClassValues, out double[] classValues, out double[] thresholds) {
-      double maxEstimatedValue = estimatedValues.Max();
-      double minEstimatedValue = estimatedValues.Min();
       var estimatedTargetValues = Enumerable.Zip(estimatedValues, targetClassValues, (e, t) => new { EstimatedValue = e, TargetValue = t }).ToList();
+      double estimatedValuesRange = estimatedValues.Range();
 
       Dictionary<double, double> classMean = new Dictionary<double, double>();
@@ -82,36 +81,45 @@
           // calculate all thresholds
           CalculateCutPoints(classMean[class0], classStdDev[class0], classMean[class1], classStdDev[class1], out x1, out x2);
-          if (!thresholdList.Any(x => x.IsAlmost(x1))) thresholdList.Add(x1);
-          if (!thresholdList.Any(x => x.IsAlmost(x2))) thresholdList.Add(x2);
+
+          // if the two cut points are too close (for instance because the stdDev=0)
+          // then move them by 0.1% of the range of estimated values
+          if (x1.IsAlmost(x2)) {
+            x1 -= 0.001 * estimatedValuesRange;
+            x2 += 0.001 * estimatedValuesRange;
+          }
+          if (!double.IsInfinity(x1) && !thresholdList.Any(x => x.IsAlmost(x1))) thresholdList.Add(x1);
+          if (!double.IsInfinity(x2) && !thresholdList.Any(x => x.IsAlmost(x2))) thresholdList.Add(x2);
         }
       }
       thresholdList.Sort();
-      thresholdList.Insert(0, double.NegativeInfinity);
+
+      // add small value and large value for the calculation of most influential class in each thresholded section
+      thresholdList.Insert(0, estimatedValues.Min() - 1);
+      thresholdList.Add(estimatedValues.Max() + 1);
 
       // determine class values for each partition separated by a threshold by calculating the density of all class distributions
       // all points in the partition are classified as the class with the maximal density in the parition
       List<double> classValuesList = new List<double>();
-      for (int i = 0; i < thresholdList.Count; i++) {
-        double m;
-        if (double.IsNegativeInfinity(thresholdList[i])) {
-          m = thresholdList[i + 1] - 1.0; // smaller than the smalles non-infinity threshold
-        } else if (i == thresholdList.Count - 1) {
-          // last threshold
-          m = thresholdList[i] + 1.0; // larger than the last threshold
-        } else {
-          m = thresholdList[i] + (thresholdList[i + 1] - thresholdList[i]) / 2.0; // middle of partition
+      if (thresholdList.Count == 2) {
+        // this happens if there are no thresholds (distributions for all classes are exactly the same)
+        // -> all samples should be classified as the first class
+        classValuesList.Add(originalClasses[0]);
+      } else {
+        // at least one reasonable threshold ...
+        // find the most likely class for the points between thresholds m
+        for (int i = 0; i < thresholdList.Count - 1; i++) {
+
+          // determine class with maximal density mass between the thresholds
+          double maxDensity = LogNormalDensityMass(thresholdList[i], thresholdList[i + 1], classMean[originalClasses[0]], classStdDev[originalClasses[0]]);
+          double maxDensityClassValue = originalClasses[0];
+          foreach (var classValue in originalClasses.Skip(1)) {
+            double density = LogNormalDensityMass(thresholdList[i], thresholdList[i + 1], classMean[classValue], classStdDev[classValue]);
+            if (density > maxDensity) {
+              maxDensity = density;
+              maxDensityClassValue = classValue;
+            }
+          }
+          classValuesList.Add(maxDensityClassValue);
         }
-
-        // determine class with maximal probability density in m
-        double maxDensity = double.MinValue;
-        double maxDensityClassValue = -1;
-        foreach (var classValue in originalClasses) {
-          double density = NormalDensity(m, classMean[classValue], classStdDev[classValue]);
-          if (density > maxDensity) {
-            maxDensity = density;
-            maxDensityClassValue = classValue;
-          }
-        }
-        classValuesList.Add(maxDensityClassValue);
       }
 
@@ -125,10 +133,12 @@
       //    /   / /\s  \ \     
       //  -/---/-/ -\---\-\----
+
       List<double> filteredThresholds = new List<double>();
       List<double> filteredClassValues = new List<double>();
-      filteredThresholds.Add(thresholdList[0]);
+      filteredThresholds.Add(double.NegativeInfinity); // the smallest possible threshold for the first class
       filteredClassValues.Add(classValuesList[0]);
+      // do not include the last threshold which was just needed for the previous step
       for (int i = 0; i < classValuesList.Count - 1; i++) {
-        if (classValuesList[i] != classValuesList[i + 1]) {
+        if (!classValuesList[i].IsAlmost(classValuesList[i + 1])) {
           filteredThresholds.Add(thresholdList[i + 1]);
           filteredClassValues.Add(classValuesList[i + 1]);
@@ -139,16 +149,45 @@
     }
 
-    private static double NormalDensity(double x, double mu, double sigma) {
+    private static double LogNormalDensityMass(double lower, double upper, double mu, double sigma) {
       if (sigma.IsAlmost(0.0)) {
-        if (x.IsAlmost(mu)) return 1.0; else return 0.0;
-      } else {
-        return (1.0 / Math.Sqrt(2.0 * Math.PI * sigma * sigma)) * Math.Exp(-((x - mu) * (x - mu)) / (2.0 * sigma * sigma));
+        if (lower < mu && mu < upper) return double.PositiveInfinity; // log(1)
+        else return double.NegativeInfinity; // log(0)
       }
+
+      Func<double, double> f = (x) =>
+        x * -0.5 * Math.Log(2.0 * Math.PI * sigma * sigma) - Math.Pow(x - mu, 3) / (3 * 2.0 * sigma * sigma);
+
+      if (double.IsNegativeInfinity(lower)) return f(upper);
+      else return f(upper) - f(lower);
     }
 
     private static void CalculateCutPoints(double m1, double s1, double m2, double s2, out double x1, out double x2) {
-      double a = (s1 * s1 - s2 * s2);
-      x1 = -(-m2 * s1 * s1 + m1 * s2 * s2 + Math.Sqrt(s1 * s1 * s2 * s2 * ((m1 - m2) * (m1 - m2) + 2.0 * (-s1 * s1 + s2 * s2) * Math.Log(s2 / s1)))) / a;
-      x2 = (m2 * s1 * s1 - m1 * s2 * s2 + Math.Sqrt(s1 * s1 * s2 * s2 * ((m1 - m2) * (m1 - m2) + 2.0 * (-s1 * s1 + s2 * s2) * Math.Log(s2 / s1)))) / a;
+      if (s1.IsAlmost(s2)) {
+        if (m1.IsAlmost(m2)) {
+          x1 = double.NegativeInfinity;
+          x2 = double.NegativeInfinity;
+        } else {
+          x1 = (m1 + m2) / 2;
+          x2 = double.NegativeInfinity;
+        }
+      } else if (s1.IsAlmost(0.0)) {
+        x1 = m1;
+        x2 = m1;
+      } else if (s2.IsAlmost(0.0)) {
+        x1 = m2;
+        x2 = m2;
+      } else {
+        if (s2 < s1) {
+          // make sure s2 is the larger std.dev.
+          CalculateCutPoints(m2, s2, m1, s1, out x1, out x2);
+        } else {
+          double a = (s1 + s2) * (s1 - s2);
+          double g = Math.Sqrt(s1 * s1 * s2 * s2 * ((m1 - m2) * (m1 - m2) + 2.0 * (s1 * s1 + s2 * s2) * Math.Log(s2 / s1)));
+          double m1s2 = m1 * s2 * s2;
+          double m2s1 = m2 * s1 * s1;
+          x1 = -(-m2s1 + m1s2 + g) / a;
+          x2 = (m2s1 - m1s2 + g) / a;
+        }
+      }
     }
   }
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/ClassificationWeightCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/ClassificationWeightCalculator.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/ClassificationWeightCalculator.cs	(revision 8811)
@@ -26,5 +26,4 @@
 using HeuristicLab.Data;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.Problems.DataAnalysis.Interfaces;
 
 namespace HeuristicLab.Problems.DataAnalysis {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/DiscriminantClassificationWeightCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/DiscriminantClassificationWeightCalculator.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/WeightCalculators/DiscriminantClassificationWeightCalculator.cs	(revision 8811)
@@ -24,5 +24,4 @@
 using HeuristicLab.Common;
 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
-using HeuristicLab.Problems.DataAnalysis.Interfaces;
 
 namespace HeuristicLab.Problems.DataAnalysis {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblem.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblem.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblem.cs	(revision 8811)
@@ -66,4 +66,9 @@
     }
 
+    protected DataAnalysisProblem(T problemData)
+      : this() {
+      ProblemData = problemData;
+    }
+
     [StorableHook(HookType.AfterDeserialization)]
     private void AfterDeserialization() {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblemData.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblemData.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/DataAnalysisProblemData.cs	(revision 8811)
@@ -107,4 +107,5 @@
     [StorableConstructor]
     protected DataAnalysisProblemData(bool deserializing) : base(deserializing) { }
+
     [StorableHook(HookType.AfterDeserialization)]
     private void AfterDeserialization() {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs	(revision 8811)
@@ -30,5 +30,5 @@
   public class ConstantRegressionModel : NamedItem, IRegressionModel {
     [Storable]
-    private double constant;
+    protected double constant;
     public double Constant {
       get { return constant; }
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleSolution.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleSolution.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionEnsembleSolution.cs	(revision 8811)
@@ -36,7 +36,8 @@
   [Item("Regression Ensemble Solution", "A regression solution that contains an ensemble of multiple regression models")]
   [Creatable("Data Analysis - Ensembles")]
-  public sealed class RegressionEnsembleSolution : RegressionSolution, IRegressionEnsembleSolution {
+  public sealed class RegressionEnsembleSolution : RegressionSolutionBase, IRegressionEnsembleSolution {
     private readonly Dictionary<int, double> trainingEvaluationCache = new Dictionary<int, double>();
     private readonly Dictionary<int, double> testEvaluationCache = new Dictionary<int, double>();
+    private readonly Dictionary<int, double> evaluationCache = new Dictionary<int, double>();
 
     public new IRegressionEnsembleModel Model {
@@ -155,9 +156,9 @@
     }
 
-    protected override void RecalculateResults() {
-      CalculateResults();
-    }
-
     #region Evaluation
+    public override IEnumerable<double> EstimatedValues {
+      get { return GetEstimatedValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
+    }
+
     public override IEnumerable<double> EstimatedTrainingValues {
       get {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionProblem.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionProblem.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionProblem.cs	(revision 8811)
@@ -36,8 +36,6 @@
     public override IDeepCloneable Clone(Cloner cloner) { return new RegressionProblem(this, cloner); }
 
-    public RegressionProblem()
-      : base() {
-      ProblemData = new RegressionProblemData();
-    }
+    public RegressionProblem() : base(new RegressionProblemData()) { }
+
   }
 }
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolution.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolution.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolution.cs	(revision 8811)
@@ -45,9 +45,7 @@
       : base(model, problemData) {
       evaluationCache = new Dictionary<int, double>(problemData.Dataset.Rows);
+      CalculateRegressionResults();
     }
 
-    protected override void RecalculateResults() {
-      CalculateResults();
-    }
 
     public override IEnumerable<double> EstimatedValues {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/RegressionSolutionBase.cs	(revision 8811)
@@ -29,16 +29,29 @@
   [StorableClass]
   public abstract class RegressionSolutionBase : DataAnalysisSolution, IRegressionSolution {
-    private const string TrainingMeanSquaredErrorResultName = "Mean squared error (training)";
-    private const string TestMeanSquaredErrorResultName = "Mean squared error (test)";
-    private const string TrainingMeanAbsoluteErrorResultName = "Mean absolute error (training)";
-    private const string TestMeanAbsoluteErrorResultName = "Mean absolute error (test)";
-    private const string TrainingSquaredCorrelationResultName = "Pearson's R² (training)";
-    private const string TestSquaredCorrelationResultName = "Pearson's R² (test)";
-    private const string TrainingRelativeErrorResultName = "Average relative error (training)";
-    private const string TestRelativeErrorResultName = "Average relative error (test)";
-    private const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
-    private const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
-    private const string TrainingMeanErrorResultName = "Mean error (training)";
-    private const string TestMeanErrorResultName = "Mean error (test)";
+    protected const string TrainingMeanSquaredErrorResultName = "Mean squared error (training)";
+    protected const string TestMeanSquaredErrorResultName = "Mean squared error (test)";
+    protected const string TrainingMeanAbsoluteErrorResultName = "Mean absolute error (training)";
+    protected const string TestMeanAbsoluteErrorResultName = "Mean absolute error (test)";
+    protected const string TrainingSquaredCorrelationResultName = "Pearson's R² (training)";
+    protected const string TestSquaredCorrelationResultName = "Pearson's R² (test)";
+    protected const string TrainingRelativeErrorResultName = "Average relative error (training)";
+    protected const string TestRelativeErrorResultName = "Average relative error (test)";
+    protected const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
+    protected const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
+    protected const string TrainingMeanErrorResultName = "Mean error (training)";
+    protected const string TestMeanErrorResultName = "Mean error (test)";
+
+    protected const string TrainingMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the training partition";
+    protected const string TestMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the test partition";
+    protected const string TrainingMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the training partition";
+    protected const string TestMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the test partition";
+    protected const string TrainingSquaredCorrelationResultDescription = "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition";
+    protected const string TestSquaredCorrelationResultDescription = "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition";
+    protected const string TrainingRelativeErrorResultDescription = "Average of the relative errors of the model output and the actual values on the training partition";
+    protected const string TestRelativeErrorResultDescription = "Average of the relative errors of the model output and the actual values on the test partition";
+    protected const string TrainingNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the training partition";
+    protected const string TestNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the test partition";
+    protected const string TrainingMeanErrorResultDescription = "Mean of errors of the model on the training partition";
+    protected const string TestMeanErrorResultDescription = "Mean of errors of the model on the test partition";
 
     public new IRegressionModel Model {
@@ -115,16 +128,16 @@
     protected RegressionSolutionBase(IRegressionModel model, IRegressionProblemData problemData)
       : base(model, problemData) {
-      Add(new Result(TrainingMeanSquaredErrorResultName, "Mean of squared errors of the model on the training partition", new DoubleValue()));
-      Add(new Result(TestMeanSquaredErrorResultName, "Mean of squared errors of the model on the test partition", new DoubleValue()));
-      Add(new Result(TrainingMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the training partition", new DoubleValue()));
-      Add(new Result(TestMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the test partition", new DoubleValue()));
-      Add(new Result(TrainingSquaredCorrelationResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition", new DoubleValue()));
-      Add(new Result(TestSquaredCorrelationResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition", new DoubleValue()));
-      Add(new Result(TrainingRelativeErrorResultName, "Average of the relative errors of the model output and the actual values on the training partition", new PercentValue()));
-      Add(new Result(TestRelativeErrorResultName, "Average of the relative errors of the model output and the actual values on the test partition", new PercentValue()));
-      Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, "Normalized mean of squared errors of the model on the training partition", new DoubleValue()));
-      Add(new Result(TestNormalizedMeanSquaredErrorResultName, "Normalized mean of squared errors of the model on the test partition", new DoubleValue()));
-      Add(new Result(TrainingMeanErrorResultName, "Mean of errors of the model on the training partition", new DoubleValue()));
-      Add(new Result(TestMeanErrorResultName, "Mean of errors of the model on the test partition", new DoubleValue()));
+      Add(new Result(TrainingMeanSquaredErrorResultName, TrainingMeanSquaredErrorResultDescription, new DoubleValue()));
+      Add(new Result(TestMeanSquaredErrorResultName, TestMeanSquaredErrorResultDescription, new DoubleValue()));
+      Add(new Result(TrainingMeanAbsoluteErrorResultName, TrainingMeanAbsoluteErrorResultDescription, new DoubleValue()));
+      Add(new Result(TestMeanAbsoluteErrorResultName, TestMeanAbsoluteErrorResultDescription, new DoubleValue()));
+      Add(new Result(TrainingSquaredCorrelationResultName, TrainingSquaredCorrelationResultDescription, new DoubleValue()));
+      Add(new Result(TestSquaredCorrelationResultName, TestSquaredCorrelationResultDescription, new DoubleValue()));
+      Add(new Result(TrainingRelativeErrorResultName, TrainingRelativeErrorResultDescription, new PercentValue()));
+      Add(new Result(TestRelativeErrorResultName, TestRelativeErrorResultDescription, new PercentValue()));
+      Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, TrainingNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
+      Add(new Result(TestNormalizedMeanSquaredErrorResultName, TestNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
+      Add(new Result(TrainingMeanErrorResultName, TrainingMeanErrorResultDescription, new DoubleValue()));
+      Add(new Result(TestMeanErrorResultName, TestMeanErrorResultDescription, new DoubleValue()));
     }
 
@@ -164,5 +177,9 @@
     }
 
-    protected void CalculateResults() {
+    protected override void RecalculateResults() {
+      CalculateRegressionResults();
+    }
+
+    protected void CalculateRegressionResults() {
       IEnumerable<double> estimatedTrainingValues = EstimatedTrainingValues; // cache values
       IEnumerable<double> originalTrainingValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices);
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/ConstantTimeSeriesPrognosisModel.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/ConstantTimeSeriesPrognosisModel.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/ConstantTimeSeriesPrognosisModel.cs	(revision 8811)
@@ -0,0 +1,49 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  [StorableClass]
+  [Item("Constant TimeSeries Model", "A time series model that returns for all prediciton the same constant value.")]
+  public class ConstantTimeSeriesPrognosisModel : ConstantRegressionModel, ITimeSeriesPrognosisModel {
+    [StorableConstructor]
+    protected ConstantTimeSeriesPrognosisModel(bool deserializing) : base(deserializing) { }
+    protected ConstantTimeSeriesPrognosisModel(ConstantTimeSeriesPrognosisModel original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new ConstantTimeSeriesPrognosisModel(this, cloner);
+    }
+
+    public ConstantTimeSeriesPrognosisModel(double constant) : base(constant) { }
+
+    public IEnumerable<IEnumerable<double>> GetPrognosedValues(Dataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
+      return horizons.Select(horizon => Enumerable.Repeat(Constant, horizon));
+    }
+
+    public ITimeSeriesPrognosisSolution CreateTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData) {
+      return new TimeSeriesPrognosisSolution(this, problemData);
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/TimeSeriesPrognosisAutoRegressiveModel.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/TimeSeriesPrognosisAutoRegressiveModel.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/TimeSeriesPrognosisAutoRegressiveModel.cs	(revision 8811)
@@ -0,0 +1,118 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  [StorableClass]
+  [Item("Autoregressive TimeSeries Model", "A linear autoregressive time series model used to predict future values.")]
+  public class TimeSeriesPrognosisAutoRegressiveModel : NamedItem, ITimeSeriesPrognosisModel {
+    [Storable]
+    public double[] Phi { get; private set; }
+    [Storable]
+    public double Constant { get; private set; }
+    [Storable]
+    public string TargetVariable { get; private set; }
+
+    public int TimeOffset { get { return Phi.Length; } }
+
+    [StorableConstructor]
+    protected TimeSeriesPrognosisAutoRegressiveModel(bool deserializing) : base(deserializing) { }
+    protected TimeSeriesPrognosisAutoRegressiveModel(TimeSeriesPrognosisAutoRegressiveModel original, Cloner cloner)
+      : base(original, cloner) {
+      this.Phi = (double[])original.Phi.Clone();
+      this.Constant = original.Constant;
+      this.TargetVariable = original.TargetVariable;
+    }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new TimeSeriesPrognosisAutoRegressiveModel(this, cloner);
+    }
+    public TimeSeriesPrognosisAutoRegressiveModel(string targetVariable, double[] phi, double constant)
+      : base("AR(1) Model") {
+      Phi = (double[])phi.Clone();
+      Constant = constant;
+      TargetVariable = targetVariable;
+    }
+
+    public IEnumerable<IEnumerable<double>> GetPrognosedValues(Dataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
+      var rowsEnumerator = rows.GetEnumerator();
+      var horizonsEnumerator = horizons.GetEnumerator();
+      var targetValues = dataset.GetReadOnlyDoubleValues(TargetVariable);
+      // produce a n-step forecast for all rows
+      while (rowsEnumerator.MoveNext() & horizonsEnumerator.MoveNext()) {
+        int row = rowsEnumerator.Current;
+        int horizon = horizonsEnumerator.Current;
+        if (row - TimeOffset < 0) {
+          yield return Enumerable.Repeat(double.NaN, horizon);
+          continue;
+        }
+
+        double[] prognosis = new double[horizon];
+        for (int h = 0; h < horizon; h++) {
+          double estimatedValue = 0.0;
+          for (int i = 1; i <= TimeOffset; i++) {
+            int offset = h - i;
+            if (offset >= 0) estimatedValue += prognosis[offset] * Phi[i - 1];
+            else estimatedValue += targetValues[row + offset] * Phi[i - 1];
+
+          }
+          estimatedValue += Constant;
+          prognosis[h] = estimatedValue;
+        }
+
+        yield return prognosis;
+      }
+
+      if (rowsEnumerator.MoveNext() || horizonsEnumerator.MoveNext())
+        throw new ArgumentException("Number of elements in rows and horizon enumerations doesn't match.");
+    }
+
+    public IEnumerable<double> GetEstimatedValues(Dataset dataset, IEnumerable<int> rows) {
+      var targetVariables = dataset.GetReadOnlyDoubleValues(TargetVariable);
+      foreach (int row in rows) {
+        double estimatedValue = 0.0;
+        if (row - TimeOffset < 0) {
+          yield return double.NaN;
+          continue;
+        }
+
+        for (int i = 1; i <= TimeOffset; i++) {
+          estimatedValue += targetVariables[row - i] * Phi[i - 1];
+        }
+        estimatedValue += Constant;
+        yield return estimatedValue;
+      }
+    }
+
+    public ITimeSeriesPrognosisSolution CreateTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData) {
+      return new TimeSeriesPrognosisSolution(this, problemData);
+    }
+    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
+      throw new NotSupportedException();
+    }
+
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblem.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblem.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblem.cs	(revision 8811)
@@ -0,0 +1,38 @@
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  [StorableClass]
+  [Item("Time-series Prognosis Problem", "A general time-series prognosis problem.")]
+  [Creatable("Problems")]
+  public class TimeSeriesPrognosisProblem : DataAnalysisProblem<ITimeSeriesPrognosisProblemData>, ITimeSeriesPrognosisProblem {
+    [StorableConstructor]
+    protected TimeSeriesPrognosisProblem(bool deserializing) : base(deserializing) { }
+    protected TimeSeriesPrognosisProblem(TimeSeriesPrognosisProblem original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) { return new TimeSeriesPrognosisProblem(this, cloner); }
+
+    public TimeSeriesPrognosisProblem() : base(new TimeSeriesPrognosisProblemData()) { }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblemData.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblemData.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisProblemData.cs	(revision 8811)
@@ -0,0 +1,1612 @@
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Parameters;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  [StorableClass]
+  [Item("TimeSeriesPrognosisProblemData", "Represents an item containing all data defining a time series prognosis problem.")]
+  public class TimeSeriesPrognosisProblemData : RegressionProblemData, ITimeSeriesPrognosisProblemData {
+    #region default data
+    private static double[,] mackey_glass_17 = new double[,] {
+{9.0000000e-01},
+{9.0000000e-01},
+{9.0000000e-01},
+{9.4136145e-01},
+{1.1188041e+00},
+{1.2161867e+00},
+{1.2476827e+00},
+{1.0067861e+00},
+{7.1297054e-01},
+{5.0349420e-01},
+{5.8392542e-01},
+{9.4327347e-01},
+{1.0178784e+00},
+{1.0346794e+00},
+{1.1640420e+00},
+{1.0670009e+00},
+{1.0026658e+00},
+{7.7000347e-01},
+{7.0782984e-01},
+{7.9370192e-01},
+{1.0558440e+00},
+{1.2000510e+00},
+{1.2998549e+00},
+{1.1857500e+00},
+{8.4250325e-01},
+{5.5885374e-01},
+{4.2572016e-01},
+{7.5043060e-01},
+{9.7291951e-01},
+{9.3785503e-01},
+{1.0653587e+00},
+{1.1355961e+00},
+{1.1533842e+00},
+{1.0678134e+00},
+{8.1349649e-01},
+{6.6246660e-01},
+{6.2210481e-01},
+{9.3890430e-01},
+{1.1287058e+00},
+{1.1699535e+00},
+{1.2412759e+00},
+{9.8090748e-01},
+{7.3311051e-01},
+{5.2596003e-01},
+{6.3134850e-01},
+{9.7849655e-01},
+{1.0578901e+00},
+{1.0888001e+00},
+{1.1681297e+00},
+{9.9896471e-01},
+{8.7928027e-01},
+{6.6864116e-01},
+{7.3694626e-01},
+{9.7850487e-01},
+{1.1571565e+00},
+{1.2440406e+00},
+{1.2532851e+00},
+{9.3967388e-01},
+{6.5403177e-01},
+{4.5549437e-01},
+{6.4110939e-01},
+{9.6727507e-01},
+{9.8419122e-01},
+{1.0332443e+00},
+{1.1440570e+00},
+{1.0922307e+00},
+{1.0485820e+00},
+{8.0447560e-01},
+{7.1465526e-01},
+{7.1296010e-01},
+{9.9300973e-01},
+{1.1766813e+00},
+{1.2618288e+00},
+{1.2517022e+00},
+{9.2078170e-01},
+{6.2369710e-01},
+{4.3562308e-01},
+{6.4988558e-01},
+{9.5895791e-01},
+{9.5866122e-01},
+{1.0183775e+00},
+{1.1415533e+00},
+{1.1289579e+00},
+{1.1022163e+00},
+{8.4414928e-01},
+{6.9891813e-01},
+{6.2493643e-01},
+{9.0049230e-01},
+{1.1246569e+00},
+{1.1850486e+00},
+{1.2646400e+00},
+{1.0193893e+00},
+{7.3830722e-01},
+{5.1921750e-01},
+{5.6836076e-01},
+{9.3609361e-01},
+{1.0324202e+00},
+{1.0360676e+00},
+{1.1675966e+00},
+{1.0504944e+00},
+{9.8129158e-01},
+{7.5798016e-01},
+{7.1660079e-01},
+{8.3950658e-01},
+{1.0870027e+00},
+{1.2170776e+00},
+{1.3123779e+00},
+{1.1361782e+00},
+{7.9337369e-01},
+{5.2230472e-01},
+{4.3857925e-01},
+{8.0873950e-01},
+{9.7422631e-01},
+{9.3291515e-01},
+{1.0947530e+00},
+{1.1335086e+00},
+{1.1645714e+00},
+{1.0315895e+00},
+{7.9058261e-01},
+{6.3878936e-01},
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+{9.7290237e-01},
+{1.1330941e+00},
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+{4.6962326e-01},
+{8.4775775e-01},
+{9.9325612e-01},
+{9.6094332e-01},
+{1.1271528e+00},
+{1.1156670e+00},
+{1.1183008e+00},
+{9.4351589e-01},
+{7.5035067e-01},
+{6.6773972e-01},
+{7.8886291e-01},
+{1.0750322e+00},
+{1.1971741e+00},
+{1.2770426e+00},
+{1.1387571e+00},
+{8.0837143e-01},
+{5.5509702e-01},
+{4.5955065e-01},
+{8.1398530e-01},
+{1.0008314e+00},
+{9.7057086e-01},
+{1.1195985e+00},
+{1.1149111e+00},
+{1.0957539e+00},
+{9.4451157e-01},
+{7.4819097e-01},
+{6.9474604e-01},
+{8.1250200e-01},
+{1.0876804e+00},
+{1.2158509e+00},
+{1.2988266e+00},
+{1.1270851e+00},
+{7.8595861e-01},
+{5.2690470e-01},
+{4.5032342e-01},
+{8.2266007e-01},
+{9.8371303e-01},
+{9.4630598e-01},
+{1.1090276e+00}};
+
+    private static readonly Dataset defaultDataset;
+    private static readonly IEnumerable<string> defaultAllowedInputVariables;
+    private static readonly string defaultTargetVariable;
+
+    static TimeSeriesPrognosisProblemData() {
+      defaultDataset = new Dataset(new string[] { "x" }, mackey_glass_17);
+      defaultDataset.Name = "Mackey-Glass (t=17) Time Series Benchmark Dataset";
+      defaultAllowedInputVariables = Enumerable.Empty<string>();
+      defaultTargetVariable = "x";
+    }
+    #endregion
+
+    private const string TrainingHorizonParameterName = "Training Horizon";
+    private const string TestHorizonParameterName = "Test Horizon";
+
+    public IFixedValueParameter<IntValue> TrainingHorizonParameter {
+      get { return (IFixedValueParameter<IntValue>)Parameters[TrainingHorizonParameterName]; }
+    }
+    public IFixedValueParameter<IntValue> TestHorizonParameter {
+      get { return (IFixedValueParameter<IntValue>)Parameters[TestHorizonParameterName]; }
+    }
+
+    #region Properties
+    public int TrainingHorizon {
+      get { return TrainingHorizonParameter.Value.Value; }
+      set { TrainingHorizonParameter.Value.Value = value; }
+    }
+
+    public int TestHorizon {
+      get { return TestHorizonParameter.Value.Value; }
+      set { TestHorizonParameter.Value.Value = value; }
+    }
+    #endregion
+
+    [StorableConstructor]
+    protected TimeSeriesPrognosisProblemData(bool deserializing) : base(deserializing) { }
+
+    protected TimeSeriesPrognosisProblemData(TimeSeriesPrognosisProblemData original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new TimeSeriesPrognosisProblemData(this, cloner);
+    }
+
+    public TimeSeriesPrognosisProblemData()
+      : this(defaultDataset, defaultAllowedInputVariables, defaultTargetVariable) {
+      TrainingPartition.Start = 50;
+    }
+    public TimeSeriesPrognosisProblemData(Dataset dataset, IEnumerable<string> allowedInputVariables, string targetVariable)
+      : base(dataset, allowedInputVariables, targetVariable) {
+      Parameters.Add(new FixedValueParameter<IntValue>(TrainingHorizonParameterName, "Specifies the horizon (how far the prognosis reaches in the future) for each training sample.", new IntValue(1)));
+      Parameters.Add(new FixedValueParameter<IntValue>(TestHorizonParameterName, "Specifies the horizon (how far the prognosis reaches in the future) for each test sample.", new IntValue(1)));
+
+      TrainingHorizonParameter.Hidden = true;
+      TestHorizonParameter.Hidden = true;
+
+      TrainingPartition.Start = Math.Min((int)(TrainingPartition.Size * 0.2), 50);
+
+      RegisterParameterEventHandlers();
+    }
+
+    [StorableHook(HookType.AfterDeserialization)]
+    private void AfterDeserialization() {
+      RegisterParameterEventHandlers();
+    }
+
+    private void RegisterParameterEventHandlers() {
+      TrainingHorizonParameter.Value.ValueChanged += new System.EventHandler(Parameter_ValueChanged);
+      TestHorizonParameter.Value.ValueChanged += new System.EventHandler(Parameter_ValueChanged);
+    }
+
+    private void Parameter_ValueChanged(object sender, EventArgs e) {
+      OnChanged();
+    }
+
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs	(revision 8811)
@@ -0,0 +1,456 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Data;
+using HeuristicLab.Optimization;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  [StorableClass]
+  [Item("Prognosis Results", "Represents a collection of time series prognosis results.")]
+  public class TimeSeriesPrognosisResults : ResultCollection {
+    #region result names
+    protected const string PrognosisTrainingMeanSquaredErrorResultName = "Mean squared error (training)";
+    protected const string PrognosisTestMeanSquaredErrorResultName = "Mean squared error (test)";
+    protected const string PrognosisTrainingMeanAbsoluteErrorResultName = "Mean absolute error (training)";
+    protected const string PrognosisTestMeanAbsoluteErrorResultName = "Mean absolute error (test)";
+    protected const string PrognosisTrainingSquaredCorrelationResultName = "Pearson's R² (training)";
+    protected const string PrognosisTestSquaredCorrelationResultName = "Pearson's R² (test)";
+    protected const string PrognosisTrainingRelativeErrorResultName = "Average relative error (training)";
+    protected const string PrognosisTestRelativeErrorResultName = "Average relative error (test)";
+    protected const string PrognosisTrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
+    protected const string PrognosisTestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
+    protected const string PrognosisTrainingMeanErrorResultName = "Mean error (training)";
+    protected const string PrognosisTestMeanErrorResultName = "Mean error (test)";
+
+    protected const string PrognosisTrainingDirectionalSymmetryResultName = "Average directional symmetry (training)";
+    protected const string PrognosisTestDirectionalSymmetryResultName = "Average directional symmetry (test)";
+    protected const string PrognosisTrainingWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (training)";
+    protected const string PrognosisTestWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (test)";
+    protected const string PrognosisTrainingTheilsUStatisticAR1ResultName = "Theil's U2 (AR1) (training)";
+    protected const string PrognosisTestTheilsUStatisticAR1ResultName = "Theil's U2 (AR1) (test)";
+    protected const string PrognosisTrainingTheilsUStatisticMeanResultName = "Theil's U2 (mean) (training)";
+    protected const string PrognosisTestTheilsUStatisticMeanResultName = "Theil's U2 (mean) (test)";
+    #endregion
+
+    #region result descriptions
+    protected const string PrognosisTrainingMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the training partition";
+    protected const string PrognosisTestMeanSquaredErrorResultDescription = "Mean of squared errors of the model on the test partition";
+    protected const string PrognosisTrainingMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the training partition";
+    protected const string PrognosisTestMeanAbsoluteErrorResultDescription = "Mean of absolute errors of the model on the test partition";
+    protected const string PrognosisTrainingSquaredCorrelationResultDescription = "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition";
+    protected const string PrognosisTestSquaredCorrelationResultDescription = "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition";
+    protected const string PrognosisTrainingRelativeErrorResultDescription = "Average of the relative errors of the model output and the actual values on the training partition";
+    protected const string PrognosisTestRelativeErrorResultDescription = "Average of the relative errors of the model output and the actual values on the test partition";
+    protected const string PrognosisTrainingNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the training partition";
+    protected const string PrognosisTestNormalizedMeanSquaredErrorResultDescription = "Normalized mean of squared errors of the model on the test partition";
+    protected const string PrognosisTrainingMeanErrorResultDescription = "Mean of errors of the model on the training partition";
+    protected const string PrognosisTestMeanErrorResultDescription = "Mean of errors of the model on the test partition";
+
+    protected const string PrognosisTrainingDirectionalSymmetryResultDescription = "The average directional symmetry of the forecasts of the model on the training partition";
+    protected const string PrognosisTestDirectionalSymmetryResultDescription = "The average directional symmetry of the forecasts of the model on the test partition";
+    protected const string PrognosisTrainingWeightedDirectionalSymmetryResultDescription = "The average weighted directional symmetry of the forecasts of the model on the training partition";
+    protected const string PrognosisTestWeightedDirectionalSymmetryResultDescription = "The average weighted directional symmetry of the forecasts of the model on the test partition";
+    protected const string PrognosisTrainingTheilsUStatisticAR1ResultDescription = "The Theil's U statistic (reference: AR1 model) of the forecasts of the model on the training partition";
+    protected const string PrognosisTestTheilsUStatisticAR1ResultDescription = "The Theil's U statistic (reference: AR1 model) of the forecasts of the model on the test partition";
+    protected const string PrognosisTrainingTheilsUStatisticMeanResultDescription = "The Theil's U statistic (reference: mean model) of the forecasts of the model on the training partition";
+    protected const string PrognosisTestTheilsUStatisticMeanResultDescription = "The Theil's U statistic (reference: mean value) of the forecasts of the model on the test partition";
+    #endregion
+
+    #region result properties
+    //prognosis results for different horizons
+    public double PrognosisTrainingMeanSquaredError {
+      get {
+        if (!ContainsKey(PrognosisTrainingMeanSquaredErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTrainingMeanSquaredErrorResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTrainingMeanSquaredErrorResultName)) Add(new Result(PrognosisTrainingMeanSquaredErrorResultName, PrognosisTrainingMeanSquaredErrorResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTrainingMeanSquaredErrorResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTestMeanSquaredError {
+      get {
+        if (!ContainsKey(PrognosisTestMeanSquaredErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTestMeanSquaredErrorResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTestMeanSquaredErrorResultName)) Add(new Result(PrognosisTestMeanSquaredErrorResultName, PrognosisTestMeanSquaredErrorResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTestMeanSquaredErrorResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTrainingMeanAbsoluteError {
+      get {
+        if (!ContainsKey(PrognosisTrainingMeanAbsoluteErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTrainingMeanAbsoluteErrorResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTrainingMeanAbsoluteErrorResultName)) Add(new Result(PrognosisTrainingMeanAbsoluteErrorResultName, PrognosisTrainingMeanAbsoluteErrorResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTrainingMeanAbsoluteErrorResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTestMeanAbsoluteError {
+      get {
+        if (!ContainsKey(PrognosisTestMeanAbsoluteErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTestMeanAbsoluteErrorResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTestMeanAbsoluteErrorResultName)) Add(new Result(PrognosisTestMeanAbsoluteErrorResultName, PrognosisTestMeanAbsoluteErrorResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTestMeanAbsoluteErrorResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTrainingRSquared {
+      get {
+        if (!ContainsKey(PrognosisTrainingSquaredCorrelationResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTrainingSquaredCorrelationResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTrainingSquaredCorrelationResultName)) Add(new Result(PrognosisTrainingSquaredCorrelationResultName, PrognosisTrainingSquaredCorrelationResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTrainingSquaredCorrelationResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTestRSquared {
+      get {
+        if (!ContainsKey(PrognosisTestSquaredCorrelationResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTestSquaredCorrelationResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTestSquaredCorrelationResultName)) Add(new Result(PrognosisTestSquaredCorrelationResultName, PrognosisTestSquaredCorrelationResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTestSquaredCorrelationResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTrainingRelativeError {
+      get {
+        if (!ContainsKey(PrognosisTrainingRelativeErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTrainingRelativeErrorResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTrainingRelativeErrorResultName)) Add(new Result(PrognosisTrainingRelativeErrorResultName, PrognosisTrainingRelativeErrorResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTrainingRelativeErrorResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTestRelativeError {
+      get {
+        if (!ContainsKey(PrognosisTestRelativeErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTestRelativeErrorResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTestRelativeErrorResultName)) Add(new Result(PrognosisTestRelativeErrorResultName, PrognosisTestRelativeErrorResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTestRelativeErrorResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTrainingNormalizedMeanSquaredError {
+      get {
+        if (!ContainsKey(PrognosisTrainingNormalizedMeanSquaredErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTrainingNormalizedMeanSquaredErrorResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTrainingNormalizedMeanSquaredErrorResultName)) Add(new Result(PrognosisTrainingNormalizedMeanSquaredErrorResultName, PrognosisTrainingNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTrainingNormalizedMeanSquaredErrorResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTestNormalizedMeanSquaredError {
+      get {
+        if (!ContainsKey(PrognosisTestNormalizedMeanSquaredErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTestNormalizedMeanSquaredErrorResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTestNormalizedMeanSquaredErrorResultName)) Add(new Result(PrognosisTestNormalizedMeanSquaredErrorResultName, PrognosisTestNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTestNormalizedMeanSquaredErrorResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTrainingMeanError {
+      get {
+        if (!ContainsKey(PrognosisTrainingMeanErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTrainingMeanErrorResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTrainingMeanErrorResultName)) Add(new Result(PrognosisTrainingMeanErrorResultName, PrognosisTrainingMeanErrorResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTrainingMeanErrorResultName].Value).Value = value;
+      }
+    }
+
+    public double PrognosisTestMeanError {
+      get {
+        if (!ContainsKey(PrognosisTestMeanErrorResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTestMeanErrorResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTestMeanErrorResultName)) Add(new Result(PrognosisTestMeanErrorResultName, PrognosisTestMeanErrorResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTestMeanErrorResultName].Value).Value = value;
+      }
+    }
+
+
+    public double PrognosisTrainingDirectionalSymmetry {
+      get {
+        if (!ContainsKey(PrognosisTrainingDirectionalSymmetryResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTrainingDirectionalSymmetryResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTrainingDirectionalSymmetryResultName)) Add(new Result(PrognosisTrainingDirectionalSymmetryResultName, PrognosisTrainingDirectionalSymmetryResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTrainingDirectionalSymmetryResultName].Value).Value = value;
+      }
+    }
+    public double PrognosisTestDirectionalSymmetry {
+      get {
+        if (!ContainsKey(PrognosisTestDirectionalSymmetryResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTestDirectionalSymmetryResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTestDirectionalSymmetryResultName)) Add(new Result(PrognosisTestDirectionalSymmetryResultName, PrognosisTestDirectionalSymmetryResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTestDirectionalSymmetryResultName].Value).Value = value;
+      }
+    }
+    public double PrognosisTrainingWeightedDirectionalSymmetry {
+      get {
+        if (!ContainsKey(PrognosisTrainingWeightedDirectionalSymmetryResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTrainingWeightedDirectionalSymmetryResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTrainingWeightedDirectionalSymmetryResultName)) Add(new Result(PrognosisTrainingWeightedDirectionalSymmetryResultName, PrognosisTrainingWeightedDirectionalSymmetryResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTrainingWeightedDirectionalSymmetryResultName].Value).Value = value;
+      }
+    }
+    public double PrognosisTestWeightedDirectionalSymmetry {
+      get {
+        if (!ContainsKey(PrognosisTestWeightedDirectionalSymmetryResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTestWeightedDirectionalSymmetryResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTestWeightedDirectionalSymmetryResultName)) Add(new Result(PrognosisTestWeightedDirectionalSymmetryResultName, PrognosisTestWeightedDirectionalSymmetryResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTestWeightedDirectionalSymmetryResultName].Value).Value = value;
+      }
+    }
+    public double PrognosisTrainingTheilsUStatisticAR1 {
+      get {
+        if (!ContainsKey(PrognosisTrainingTheilsUStatisticAR1ResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTrainingTheilsUStatisticAR1ResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTrainingTheilsUStatisticAR1ResultName)) Add(new Result(PrognosisTrainingTheilsUStatisticAR1ResultName, PrognosisTrainingTheilsUStatisticAR1ResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTrainingTheilsUStatisticAR1ResultName].Value).Value = value;
+      }
+    }
+    public double PrognosisTestTheilsUStatisticAR1 {
+      get {
+        if (!ContainsKey(PrognosisTestTheilsUStatisticAR1ResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTestTheilsUStatisticAR1ResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTestTheilsUStatisticAR1ResultName)) Add(new Result(PrognosisTestTheilsUStatisticAR1ResultName, PrognosisTestTheilsUStatisticAR1ResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTestTheilsUStatisticAR1ResultName].Value).Value = value;
+      }
+    }
+    public double PrognosisTrainingTheilsUStatisticMean {
+      get {
+        if (!ContainsKey(PrognosisTrainingTheilsUStatisticMeanResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTrainingTheilsUStatisticMeanResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTrainingTheilsUStatisticMeanResultName)) Add(new Result(PrognosisTrainingTheilsUStatisticMeanResultName, PrognosisTrainingTheilsUStatisticMeanResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTrainingTheilsUStatisticMeanResultName].Value).Value = value;
+      }
+    }
+    public double PrognosisTestTheilsUStatisticMean {
+      get {
+        if (!ContainsKey(PrognosisTestTheilsUStatisticMeanResultName)) return double.NaN;
+        return ((DoubleValue)this[PrognosisTestTheilsUStatisticMeanResultName].Value).Value;
+      }
+      private set {
+        if (!ContainsKey(PrognosisTestTheilsUStatisticMeanResultName)) Add(new Result(PrognosisTestTheilsUStatisticMeanResultName, PrognosisTestTheilsUStatisticMeanResultDescription, new DoubleValue()));
+        ((DoubleValue)this[PrognosisTestTheilsUStatisticMeanResultName].Value).Value = value;
+      }
+    }
+    #endregion
+
+    [Storable]
+    private int trainingHorizon;
+    public int TrainingHorizon {
+      get { return trainingHorizon; }
+      set {
+        if (trainingHorizon != value) {
+          trainingHorizon = value;
+          OnTrainingHorizonChanged();
+        }
+      }
+    }
+
+    [Storable]
+    private int testHorizon;
+    public int TestHorizon {
+      get { return testHorizon; }
+      set {
+        if (testHorizon != value) {
+          testHorizon = value;
+          OnTestHorizonChanged();
+        }
+      }
+    }
+
+    private ITimeSeriesPrognosisSolution solution;
+    [Storable]
+    public ITimeSeriesPrognosisSolution Solution {
+      get { return solution; }
+      private set { solution = value; } //necessary for persistence
+    }
+
+    [StorableConstructor]
+    public TimeSeriesPrognosisResults(bool deserializing) : base(deserializing) { }
+    protected TimeSeriesPrognosisResults(TimeSeriesPrognosisResults original, Cloner cloner) : base(original, cloner) { }
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new TimeSeriesPrognosisResults(this, cloner);
+    }
+
+    public TimeSeriesPrognosisResults(int trainingHorizon, int testHorizon, ITimeSeriesPrognosisSolution solution)
+      : base() {
+      this.trainingHorizon = trainingHorizon;
+      this.testHorizon = testHorizon;
+      this.solution = solution;
+      CalculateTrainingPrognosisResults();
+      CalculateTestPrognosisResults();
+    }
+
+    #region events
+    public event EventHandler TrainingHorizonChanged;
+    protected virtual void OnTrainingHorizonChanged() {
+      CalculateTrainingPrognosisResults();
+      var handler = TrainingHorizonChanged;
+      if (handler != null) handler(this, EventArgs.Empty);
+    }
+
+    public event EventHandler TestHorizonChanged;
+    protected virtual void OnTestHorizonChanged() {
+      CalculateTestPrognosisResults();
+      var handler = TestHorizonChanged;
+      if (handler != null) handler(this, EventArgs.Empty);
+    }
+    #endregion
+
+    private void CalculateTrainingPrognosisResults() {
+      OnlineCalculatorError errorState;
+      var problemData = Solution.ProblemData;
+      var model = Solution.Model;
+      //mean model
+      double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
+      var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
+
+      //AR1 model
+      double alpha, beta;
+      IEnumerable<double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
+      OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
+      var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
+
+      var trainingHorizions = problemData.TrainingIndices.Select(r => Math.Min(trainingHorizon, problemData.TrainingPartition.End - r)).ToList();
+      IEnumerable<IEnumerable<double>> trainingTargetValues = problemData.TrainingIndices.Zip(trainingHorizions, Enumerable.Range).Select(r => problemData.Dataset.GetDoubleValues(problemData.TargetVariable, r)).ToList();
+      IEnumerable<IEnumerable<double>> trainingEstimatedValues = model.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
+      IEnumerable<IEnumerable<double>> trainingMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
+      IEnumerable<IEnumerable<double>> trainingAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TrainingIndices, trainingHorizions).ToList();
+
+      IEnumerable<double> originalTrainingValues = trainingTargetValues.SelectMany(x => x).ToList();
+      IEnumerable<double> estimatedTrainingValues = trainingEstimatedValues.SelectMany(x => x).ToList();
+
+      double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      PrognosisTrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
+      double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
+      double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
+      double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
+      double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      PrognosisTrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
+      double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
+      PrognosisTrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
+
+      PrognosisTrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
+      PrognosisTrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTrainingDirectionalSymmetry : 0.0;
+      PrognosisTrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
+      PrognosisTrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTrainingWeightedDirectionalSymmetry : 0.0;
+      PrognosisTrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
+      PrognosisTrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticAR1 : double.PositiveInfinity;
+      PrognosisTrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
+      PrognosisTrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticMean : double.PositiveInfinity;
+    }
+
+    private void CalculateTestPrognosisResults() {
+      OnlineCalculatorError errorState;
+      var problemData = Solution.ProblemData;
+      var model = Solution.Model;
+      //mean model
+      double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
+      var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
+
+      //AR1 model
+      double alpha, beta;
+      IEnumerable<double> trainingStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
+      OnlineLinearScalingParameterCalculator.Calculate(problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
+      var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(problemData.TargetVariable, new double[] { beta }, alpha);
+
+      var testHorizions = problemData.TestIndices.Select(r => Math.Min(testHorizon, problemData.TestPartition.End - r)).ToList();
+      IEnumerable<IEnumerable<double>> testTargetValues = problemData.TestIndices.Zip(testHorizions, Enumerable.Range).Select(r => problemData.Dataset.GetDoubleValues(problemData.TargetVariable, r)).ToList();
+      IEnumerable<IEnumerable<double>> testEstimatedValues = model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
+      IEnumerable<double> testStartValues = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TestIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
+      IEnumerable<IEnumerable<double>> testMeanModelPredictions = meanModel.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
+      IEnumerable<IEnumerable<double>> testAR1ModelPredictions = AR1model.GetPrognosedValues(problemData.Dataset, problemData.TestIndices, testHorizions).ToList();
+
+      IEnumerable<double> originalTestValues = testTargetValues.SelectMany(x => x).ToList();
+      IEnumerable<double> estimatedTestValues = testEstimatedValues.SelectMany(x => x).ToList();
+
+      double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      PrognosisTestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
+      double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      PrognosisTestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
+      double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
+      double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      PrognosisTestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
+      double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      PrognosisTestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
+      double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
+      PrognosisTestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
+
+      PrognosisTestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
+      PrognosisTestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTestDirectionalSymmetry : 0.0;
+      PrognosisTestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
+      PrognosisTestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTestWeightedDirectionalSymmetry : 0.0;
+      PrognosisTestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
+      PrognosisTestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticAR1 : double.PositiveInfinity;
+      PrognosisTestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
+      PrognosisTestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticMean : double.PositiveInfinity;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolution.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolution.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolution.cs	(revision 8811)
@@ -0,0 +1,46 @@
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+using HeuristicLab.Common;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  /// <summary>
+  /// Represents a time series prognosis data analysis solution
+  /// </summary>
+  [StorableClass]
+  public class TimeSeriesPrognosisSolution : TimeSeriesPrognosisSolutionBase {
+    [StorableConstructor]
+    protected TimeSeriesPrognosisSolution(bool deserializing) : base(deserializing) { }
+    protected TimeSeriesPrognosisSolution(TimeSeriesPrognosisSolution original, Cloner cloner) : base(original, cloner) { }
+    protected internal TimeSeriesPrognosisSolution(ITimeSeriesPrognosisModel model, ITimeSeriesPrognosisProblemData problemData)
+      : base(model, problemData) {
+      CalculateRegressionResults();
+      CalculateTimeSeriesResults();
+      CalculateTimeSeriesResults(ProblemData.TrainingHorizon, ProblemData.TestHorizon);
+    }
+
+    public override IEnumerable<IEnumerable<double>> GetPrognosedValues(IEnumerable<int> rows, IEnumerable<int> horizons) {
+      return Model.GetPrognosedValues(ProblemData.Dataset, rows, horizons);
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs	(revision 8811)
@@ -0,0 +1,197 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Data;
+using HeuristicLab.Optimization;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  [StorableClass]
+  public abstract class TimeSeriesPrognosisSolutionBase : RegressionSolutionBase, ITimeSeriesPrognosisSolution {
+    #region result names
+    protected const string TrainingDirectionalSymmetryResultName = "Average directional symmetry (training)";
+    protected const string TestDirectionalSymmetryResultName = "Average directional symmetry (test)";
+    protected const string TrainingWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (training)";
+    protected const string TestWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (test)";
+    protected const string TrainingTheilsUStatisticAR1ResultName = "Theil's U2 (AR1) (training)";
+    protected const string TestTheilsUStatisticLastResultName = "Theil's U2 (AR1) (test)";
+    protected const string TrainingTheilsUStatisticMeanResultName = "Theil's U2 (mean) (training)";
+    protected const string TestTheilsUStatisticMeanResultName = "Theil's U2 (mean) (test)";
+    protected const string TimeSeriesPrognosisResultName = "Prognosis Results";
+    #endregion
+
+    #region result descriptions
+    protected const string TrainingDirectionalSymmetryResultDescription = "The average directional symmetry of the forecasts of the model on the training partition";
+    protected const string TestDirectionalSymmetryResultDescription = "The average directional symmetry of the forecasts of the model on the test partition";
+    protected const string TrainingWeightedDirectionalSymmetryResultDescription = "The average weighted directional symmetry of the forecasts of the model on the training partition";
+    protected const string TestWeightedDirectionalSymmetryResultDescription = "The average weighted directional symmetry of the forecasts of the model on the test partition";
+    protected const string TrainingTheilsUStatisticAR1ResultDescription = "The Theil's U statistic (reference: AR1 model) of the forecasts of the model on the training partition";
+    protected const string TestTheilsUStatisticAR1ResultDescription = "The Theil's U statistic (reference: AR1 model) of the forecasts of the model on the test partition";
+    protected const string TrainingTheilsUStatisticMeanResultDescription = "The Theil's U statistic (reference: mean model) of the forecasts of the model on the training partition";
+    protected const string TestTheilsUStatisticMeanResultDescription = "The Theil's U statistic (reference: mean value) of the forecasts of the model on the test partition";
+    protected const string TimeSeriesPrognosisResultDescription = "The calculated results of predictions in the future.";
+    #endregion
+
+    public new ITimeSeriesPrognosisModel Model {
+      get { return (ITimeSeriesPrognosisModel)base.Model; }
+      protected set { base.Model = value; }
+    }
+
+    public new ITimeSeriesPrognosisProblemData ProblemData {
+      get { return (ITimeSeriesPrognosisProblemData)base.ProblemData; }
+      set { base.ProblemData = value; }
+    }
+
+    public abstract IEnumerable<IEnumerable<double>> GetPrognosedValues(IEnumerable<int> rows, IEnumerable<int> horizon);
+
+    #region Results
+    public double TrainingDirectionalSymmetry {
+      get { return ((DoubleValue)this[TrainingDirectionalSymmetryResultName].Value).Value; }
+      private set { ((DoubleValue)this[TrainingDirectionalSymmetryResultName].Value).Value = value; }
+    }
+    public double TestDirectionalSymmetry {
+      get { return ((DoubleValue)this[TestDirectionalSymmetryResultName].Value).Value; }
+      private set { ((DoubleValue)this[TestDirectionalSymmetryResultName].Value).Value = value; }
+    }
+    public double TrainingWeightedDirectionalSymmetry {
+      get { return ((DoubleValue)this[TrainingWeightedDirectionalSymmetryResultName].Value).Value; }
+      private set { ((DoubleValue)this[TrainingWeightedDirectionalSymmetryResultName].Value).Value = value; }
+    }
+    public double TestWeightedDirectionalSymmetry {
+      get { return ((DoubleValue)this[TestWeightedDirectionalSymmetryResultName].Value).Value; }
+      private set { ((DoubleValue)this[TestWeightedDirectionalSymmetryResultName].Value).Value = value; }
+    }
+    public double TrainingTheilsUStatisticAR1 {
+      get { return ((DoubleValue)this[TrainingTheilsUStatisticAR1ResultName].Value).Value; }
+      private set { ((DoubleValue)this[TrainingTheilsUStatisticAR1ResultName].Value).Value = value; }
+    }
+    public double TestTheilsUStatisticAR1 {
+      get { return ((DoubleValue)this[TestTheilsUStatisticLastResultName].Value).Value; }
+      private set { ((DoubleValue)this[TestTheilsUStatisticLastResultName].Value).Value = value; }
+    }
+    public double TrainingTheilsUStatisticMean {
+      get { return ((DoubleValue)this[TrainingTheilsUStatisticMeanResultName].Value).Value; }
+      private set { ((DoubleValue)this[TrainingTheilsUStatisticMeanResultName].Value).Value = value; }
+    }
+    public double TestTheilsUStatisticMean {
+      get { return ((DoubleValue)this[TestTheilsUStatisticMeanResultName].Value).Value; }
+      private set { ((DoubleValue)this[TestTheilsUStatisticMeanResultName].Value).Value = value; }
+    }
+
+    public TimeSeriesPrognosisResults TimeSeriesPrognosisResults {
+      get {
+        if (!ContainsKey(TimeSeriesPrognosisResultName)) return null;
+        return (TimeSeriesPrognosisResults)this[TimeSeriesPrognosisResultName];
+      }
+      set {
+        if (ContainsKey(TimeSeriesPrognosisResultName)) Remove(TimeSeriesPrognosisResultName);
+        Add(new Result(TimeSeriesPrognosisResultName, TimeSeriesPrognosisResultDescription, value));
+      }
+    }
+    #endregion
+
+
+    public override IEnumerable<double> EstimatedValues {
+      get { return GetEstimatedValues(Enumerable.Range(0, ProblemData.Dataset.Rows)); }
+    }
+    public override IEnumerable<double> EstimatedTrainingValues {
+      get { return GetEstimatedValues(ProblemData.TrainingIndices); }
+    }
+    public override IEnumerable<double> EstimatedTestValues {
+      get { return GetEstimatedValues(ProblemData.TestIndices); }
+    }
+    public override IEnumerable<double> GetEstimatedValues(IEnumerable<int> rows) {
+      return Model.GetEstimatedValues(ProblemData.Dataset, rows);
+    }
+
+    [StorableConstructor]
+    protected TimeSeriesPrognosisSolutionBase(bool deserializing) : base(deserializing) { }
+    protected TimeSeriesPrognosisSolutionBase(TimeSeriesPrognosisSolutionBase original, Cloner cloner) : base(original, cloner) { }
+    protected TimeSeriesPrognosisSolutionBase(ITimeSeriesPrognosisModel model, ITimeSeriesPrognosisProblemData problemData)
+      : base(model, problemData) {
+      Add(new Result(TrainingDirectionalSymmetryResultName, TrainingDirectionalSymmetryResultDescription, new DoubleValue()));
+      Add(new Result(TestDirectionalSymmetryResultName, TestDirectionalSymmetryResultDescription, new DoubleValue()));
+      Add(new Result(TrainingWeightedDirectionalSymmetryResultName, TrainingWeightedDirectionalSymmetryResultDescription, new DoubleValue()));
+      Add(new Result(TestWeightedDirectionalSymmetryResultName, TestWeightedDirectionalSymmetryResultDescription, new DoubleValue()));
+      Add(new Result(TrainingTheilsUStatisticAR1ResultName, TrainingTheilsUStatisticAR1ResultDescription, new DoubleValue()));
+      Add(new Result(TestTheilsUStatisticLastResultName, TestTheilsUStatisticAR1ResultDescription, new DoubleValue()));
+      Add(new Result(TrainingTheilsUStatisticMeanResultName, TrainingTheilsUStatisticMeanResultDescription, new DoubleValue()));
+      Add(new Result(TestTheilsUStatisticMeanResultName, TestTheilsUStatisticMeanResultDescription, new DoubleValue()));
+    }
+
+    protected override void RecalculateResults() {
+      base.RecalculateResults();
+      CalculateTimeSeriesResults();
+      CalculateTimeSeriesResults(ProblemData.TrainingHorizon, ProblemData.TestHorizon);
+    }
+
+    protected void CalculateTimeSeriesResults() {
+      OnlineCalculatorError errorState;
+      double trainingMean = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average();
+      var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
+
+      double alpha, beta;
+      IEnumerable<double> trainingStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
+      OnlineLinearScalingParameterCalculator.Calculate(ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
+      var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(ProblemData.TargetVariable, new double[] { beta }, alpha);
+
+
+      #region Calculate training quality measures
+      IEnumerable<double> trainingTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
+      IEnumerable<double> trainingEstimatedValues = EstimatedTrainingValues.ToList();
+      IEnumerable<double> trainingMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
+      IEnumerable<double> trainingAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
+
+      TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
+      TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0;
+      TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
+      TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0;
+      TrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
+      TrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticAR1 : double.PositiveInfinity;
+      TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
+      TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity;
+      #endregion
+
+      #region Calculate test quality measures
+      IEnumerable<double> testTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToList();
+      IEnumerable<double> testEstimatedValues = EstimatedTestValues.ToList();
+      IEnumerable<double> testMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
+      IEnumerable<double> testAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
+
+      TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
+      TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0;
+      TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
+      TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0;
+      TestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
+      TestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticAR1 : double.PositiveInfinity;
+      TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
+      TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity;
+      #endregion
+    }
+
+    protected void CalculateTimeSeriesResults(int trainingHorizon, int testHorizon) {
+      TimeSeriesPrognosisResults = new TimeSeriesPrognosisResults(trainingHorizon, testHorizon, this);
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Classification/IClassificationEnsembleSolutionWeightCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Classification/IClassificationEnsembleSolutionWeightCalculator.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Classification/IClassificationEnsembleSolutionWeightCalculator.cs	(revision 8811)
@@ -23,5 +23,5 @@
 using HeuristicLab.Core;
 
-namespace HeuristicLab.Problems.DataAnalysis.Interfaces {
+namespace HeuristicLab.Problems.DataAnalysis {
   public delegate bool CheckPoint(IClassificationProblemData problemData, int point);
 
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Classification/IDiscriminantFunctionClassificationModel.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Classification/IDiscriminantFunctionClassificationModel.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/Classification/IDiscriminantFunctionClassificationModel.cs	(revision 8811)
@@ -26,4 +26,6 @@
     IEnumerable<double> Thresholds { get; }
     IEnumerable<double> ClassValues { get; }
+    IDiscriminantFunctionThresholdCalculator ThresholdCalculator { get; }
+    void RecalculateModelParameters(IClassificationProblemData problemData, IEnumerable<int> rows);
     // class values and thresholds can only be assigned simultanously
     void SetThresholdsAndClassValues(IEnumerable<double> thresholds, IEnumerable<double> classValues);
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/IOnlineTimeSeriesCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/IOnlineTimeSeriesCalculator.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/IOnlineTimeSeriesCalculator.cs	(revision 8811)
@@ -0,0 +1,33 @@
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+
+using System;
+using System.Collections.Generic;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public interface IOnlineTimeSeriesCalculator {
+    OnlineCalculatorError ErrorState { get; }
+    double Value { get; }
+    void Reset();
+    void Add(double startValue, IEnumerable<double> actualContinuation, IEnumerable<double> predictedContinuation);
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisModel.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisModel.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisModel.cs	(revision 8811)
@@ -0,0 +1,28 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+namespace HeuristicLab.Problems.DataAnalysis {
+  public interface ITimeSeriesPrognosisModel : IRegressionModel {
+    IEnumerable<IEnumerable<double>> GetPrognosedValues(Dataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons);
+    ITimeSeriesPrognosisSolution CreateTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData);
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisProblem.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisProblem.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisProblem.cs	(revision 8811)
@@ -0,0 +1,25 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public interface ITimeSeriesPrognosisProblem : IDataAnalysisProblem<ITimeSeriesPrognosisProblemData> {
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisProblemData.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisProblemData.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisProblemData.cs	(revision 8811)
@@ -0,0 +1,27 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public interface ITimeSeriesPrognosisProblemData : IRegressionProblemData {
+    int TrainingHorizon { get; set; }
+    int TestHorizon { get; set; }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisSolution.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisSolution.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Interfaces/TimeSeriesPrognosis/ITimeSeriesPrognosisSolution.cs	(revision 8811)
@@ -0,0 +1,39 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+namespace HeuristicLab.Problems.DataAnalysis {
+  public interface ITimeSeriesPrognosisSolution : IRegressionSolution {
+    new ITimeSeriesPrognosisModel Model { get; }
+    new ITimeSeriesPrognosisProblemData ProblemData { get; set; }
+
+    IEnumerable<IEnumerable<double>> GetPrognosedValues(IEnumerable<int> rows, IEnumerable<int> horizon);
+
+    double TrainingTheilsUStatisticAR1 { get; }
+    double TestTheilsUStatisticAR1 { get; }
+    double TrainingTheilsUStatisticMean { get; }
+    double TestTheilsUStatisticMean { get; }
+    double TrainingDirectionalSymmetry { get; }
+    double TestDirectionalSymmetry { get; }
+    double TrainingWeightedDirectionalSymmetry { get; }
+    double TestWeightedDirectionalSymmetry { get; }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/AutoCorrelationCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/AutoCorrelationCalculator.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/AutoCorrelationCalculator.cs	(revision 8811)
@@ -0,0 +1,38 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Linq;
+
+namespace HeuristicLab.Problems.DataAnalysis.OnlineCalculators {
+  public class AutoCorrelationCalculator {
+    public static double[] Calculate(double[] values, out OnlineCalculatorError error) {
+      error = OnlineCalculatorError.None;
+      if (values.Any(x => double.IsNaN(x) || double.IsInfinity(x))) {
+        error = OnlineCalculatorError.InvalidValueAdded;
+        return new double[0];
+      }
+
+      double[] correlations = new double[values.Length];
+      alglib.corr.corrr1dcircular(values, values.Length, values, values.Length, ref correlations);
+      return correlations;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/HoeffdingsDependenceCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/HoeffdingsDependenceCalculator.cs	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/HoeffdingsDependenceCalculator.cs	(revision 8811)
@@ -23,5 +23,4 @@
 using System.Collections.Generic;
 using System.Linq;
-using HeuristicLab.Common;
 
 namespace HeuristicLab.Problems.DataAnalysis {
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineBoundedMeanSquaredErrorCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineBoundedMeanSquaredErrorCalculator.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineBoundedMeanSquaredErrorCalculator.cs	(revision 8811)
@@ -0,0 +1,99 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineBoundedMeanSquaredErrorCalculator : IOnlineCalculator {
+
+    private double errorSum;
+    private int n;
+    public double BoundedMeanSquaredError {
+      get {
+        return n > 0 ? errorSum / n : 0.0;
+      }
+    }
+
+    public double LowerBound { get; private set; }
+    public double UpperBound { get; private set; }
+
+
+    public OnlineBoundedMeanSquaredErrorCalculator(double lowerBound, double upperbound) {
+      LowerBound = lowerBound;
+      UpperBound = upperbound;
+      Reset();
+    }
+
+    #region IOnlineCalculator Members
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
+      get { return errorState; }
+    }
+    public double Value {
+      get { return BoundedMeanSquaredError; }
+    }
+    public void Reset() {
+      n = 0;
+      errorSum = 0.0;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
+    }
+
+    public void Add(double original, double estimated) {
+      if (double.IsNaN(estimated) || double.IsInfinity(estimated) ||
+          double.IsNaN(original) || double.IsInfinity(original) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) {
+        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+      } else {
+        double error = estimated - original;
+        if (estimated < LowerBound || estimated > UpperBound)
+          errorSum += Math.Abs(error);
+        else
+          errorSum += error * error;
+        n++;
+        errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded);        // n >= 1
+      }
+    }
+    #endregion
+
+    public static double Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, double lowerBound, double upperBound, out OnlineCalculatorError errorState) {
+      IEnumerator<double> originalEnumerator = originalValues.GetEnumerator();
+      IEnumerator<double> estimatedEnumerator = estimatedValues.GetEnumerator();
+      OnlineBoundedMeanSquaredErrorCalculator boundedMseCalculator = new OnlineBoundedMeanSquaredErrorCalculator(lowerBound, upperBound);
+
+      // always move forward both enumerators (do not use short-circuit evaluation!)
+      while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
+        double original = originalEnumerator.Current;
+        double estimated = estimatedEnumerator.Current;
+        boundedMseCalculator.Add(original, estimated);
+        if (boundedMseCalculator.ErrorState != OnlineCalculatorError.None) break;
+      }
+
+      // check if both enumerators are at the end to make sure both enumerations have the same length
+      if (boundedMseCalculator.ErrorState == OnlineCalculatorError.None &&
+         (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext())) {
+        throw new ArgumentException("Number of elements in originalValues and estimatedValues enumerations doesn't match.");
+      } else {
+        errorState = boundedMseCalculator.ErrorState;
+        return boundedMseCalculator.BoundedMeanSquaredError;
+      }
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineDirectionalSymmetryCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineDirectionalSymmetryCalculator.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineDirectionalSymmetryCalculator.cs	(revision 8811)
@@ -0,0 +1,119 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Common;
+
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineDirectionalSymmetryCalculator : IOnlineTimeSeriesCalculator {
+    private int n;
+    private int nCorrect;
+
+    public double DirectionalSymmetry {
+      get {
+        if (n < 1) return 0.0;
+        return (double)nCorrect / n;
+      }
+    }
+
+    public OnlineDirectionalSymmetryCalculator() {
+      Reset();
+    }
+
+    public double Value {
+      get { return DirectionalSymmetry; }
+    }
+
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
+      get { return errorState; }
+    }
+
+    public void Add(double startValue, IEnumerable<double> actualContinuation, IEnumerable<double> predictedContinuation) {
+      if (double.IsNaN(startValue) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) {
+        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+      } else {
+        var actualEnumerator = actualContinuation.GetEnumerator();
+        var predictedEnumerator = predictedContinuation.GetEnumerator();
+        double prevActual = startValue;
+        double prevPredicted = startValue;
+        while (actualEnumerator.MoveNext() & predictedEnumerator.MoveNext() & errorState != OnlineCalculatorError.InvalidValueAdded) {
+          double actual = actualEnumerator.Current;
+          double predicted = predictedEnumerator.Current;
+          if (double.IsNaN(actual) || double.IsNaN(predicted)) {
+            errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+          } else {
+            // count a prediction correct if the trend (positive/negative/no change) is predicted correctly
+            if ((actual - prevActual) * (predicted - prevPredicted) > 0.0 ||
+                (actual - prevActual).IsAlmost(predicted - prevPredicted)
+              ) {
+              nCorrect++;
+            }
+            n++;
+          }
+        }
+        // check if both enumerators are at the end to make sure both enumerations have the same length
+        if (actualEnumerator.MoveNext() || predictedEnumerator.MoveNext()) {
+          errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+        } else {
+          errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded); // n >= 1
+        }
+      }
+    }
+
+    public void Reset() {
+      n = 0;
+      nCorrect = 0;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
+    }
+
+    public static double Calculate(double startValue, IEnumerable<double> actualContinuation, IEnumerable<double> predictedContinuation, out OnlineCalculatorError errorState) {
+      OnlineDirectionalSymmetryCalculator dsCalculator = new OnlineDirectionalSymmetryCalculator();
+      dsCalculator.Add(startValue, actualContinuation, predictedContinuation);
+      errorState = dsCalculator.ErrorState;
+      return dsCalculator.DirectionalSymmetry;
+    }
+    
+    public static double Calculate(IEnumerable<double> startValues, IEnumerable<IEnumerable<double>> actualContinuations, IEnumerable<IEnumerable<double>> predictedContinuations, out OnlineCalculatorError errorState) {
+      IEnumerator<double> startValueEnumerator = startValues.GetEnumerator();
+      IEnumerator<IEnumerable<double>> actualContinuationsEnumerator = actualContinuations.GetEnumerator();
+      IEnumerator<IEnumerable<double>> predictedContinuationsEnumerator = predictedContinuations.GetEnumerator();
+      OnlineDirectionalSymmetryCalculator dsCalculator = new OnlineDirectionalSymmetryCalculator();
+
+      // always move forward all enumerators (do not use short-circuit evaluation!)
+      while (startValueEnumerator.MoveNext() & actualContinuationsEnumerator.MoveNext() & predictedContinuationsEnumerator.MoveNext()) {
+        dsCalculator.Add(startValueEnumerator.Current, actualContinuationsEnumerator.Current, predictedContinuationsEnumerator.Current);
+        if (dsCalculator.ErrorState != OnlineCalculatorError.None) break;
+      }
+
+      // check if all enumerators are at the end to make sure both enumerations have the same length
+      if (dsCalculator.ErrorState == OnlineCalculatorError.None &&
+          (startValueEnumerator.MoveNext() || actualContinuationsEnumerator.MoveNext() || predictedContinuationsEnumerator.MoveNext())) {
+        throw new ArgumentException("Number of elements in startValues, actualContinuations and estimatedValues predictedContinuations doesn't match.");
+      } else {
+        errorState = dsCalculator.ErrorState;
+        return dsCalculator.DirectionalSymmetry;
+      }
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineTheilsUStatisticCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineTheilsUStatisticCalculator.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineTheilsUStatisticCalculator.cs	(revision 8811)
@@ -0,0 +1,128 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineTheilsUStatisticCalculator : IOnlineTimeSeriesCalculator {
+    private OnlineMeanAndVarianceCalculator squaredErrorMeanCalculator;
+    private OnlineMeanAndVarianceCalculator unbiasedEstimatorMeanCalculator;
+
+    public double TheilsUStatistic {
+      get {
+        return Math.Sqrt(squaredErrorMeanCalculator.Mean) / Math.Sqrt(unbiasedEstimatorMeanCalculator.Mean);
+      }
+    }
+
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
+      get { return errorState | squaredErrorMeanCalculator.MeanErrorState | unbiasedEstimatorMeanCalculator.MeanErrorState; }
+    }
+
+    public OnlineTheilsUStatisticCalculator() {
+      squaredErrorMeanCalculator = new OnlineMeanAndVarianceCalculator();
+      unbiasedEstimatorMeanCalculator = new OnlineMeanAndVarianceCalculator();
+      Reset();
+    }
+
+    #region IOnlineEvaluator Members
+    public double Value {
+      get { return TheilsUStatistic; }
+    }
+
+    public void Add(double startValue, IEnumerable<double> actualContinuation, IEnumerable<double> predictedContinuation) {
+      throw new NotSupportedException();
+    }
+
+    public void Add(double startValue, IEnumerable<double> actualContinuation, IEnumerable<double> referenceContinuation, IEnumerable<double> predictedContinuation) {
+      if (double.IsNaN(startValue) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) {
+        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+      } else {
+        var actualEnumerator = actualContinuation.GetEnumerator();
+        var predictedEnumerator = predictedContinuation.GetEnumerator();
+        var referenceEnumerator = referenceContinuation.GetEnumerator();
+        while (actualEnumerator.MoveNext() & predictedEnumerator.MoveNext() & referenceEnumerator.MoveNext()
+          & ErrorState != OnlineCalculatorError.InvalidValueAdded) {
+          double actual = actualEnumerator.Current;
+          double predicted = predictedEnumerator.Current;
+          double reference = referenceEnumerator.Current;
+          if (double.IsNaN(actual) || double.IsNaN(predicted) || double.IsNaN(reference)) {
+            errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+          } else {
+            // error of predicted change
+            double errorPredictedChange = (predicted - startValue) - (actual - startValue);
+            squaredErrorMeanCalculator.Add(errorPredictedChange * errorPredictedChange);
+
+            double errorReference = (reference - startValue) - (actual - startValue);
+            unbiasedEstimatorMeanCalculator.Add(errorReference * errorReference);
+          }
+        }
+        // check if both enumerators are at the end to make sure both enumerations have the same length
+        if (actualEnumerator.MoveNext() || predictedEnumerator.MoveNext() || referenceEnumerator.MoveNext()) {
+          errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+        } else {
+          errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded); // n >= 1
+        }
+      }
+    }
+
+
+    public void Reset() {
+      squaredErrorMeanCalculator.Reset();
+      unbiasedEstimatorMeanCalculator.Reset();
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
+    }
+
+    #endregion
+
+    public static double Calculate(double startValue, IEnumerable<double> actualContinuation, IEnumerable<double> referenceContinuation, IEnumerable<double> predictedContinuation, out OnlineCalculatorError errorState) {
+      OnlineTheilsUStatisticCalculator calculator = new OnlineTheilsUStatisticCalculator();
+      calculator.Add(startValue, actualContinuation, referenceContinuation, predictedContinuation);
+      errorState = calculator.ErrorState;
+      return calculator.TheilsUStatistic;
+    }
+
+    public static double Calculate(IEnumerable<double> startValues, IEnumerable<IEnumerable<double>> actualContinuations, IEnumerable<IEnumerable<double>> referenceContinuations, IEnumerable<IEnumerable<double>> predictedContinuations, out OnlineCalculatorError errorState) {
+      IEnumerator<double> startValueEnumerator = startValues.GetEnumerator();
+      IEnumerator<IEnumerable<double>> actualContinuationsEnumerator = actualContinuations.GetEnumerator();
+      IEnumerator<IEnumerable<double>> referenceContinuationsEnumerator = referenceContinuations.GetEnumerator();
+      IEnumerator<IEnumerable<double>> predictedContinuationsEnumerator = predictedContinuations.GetEnumerator();
+
+      OnlineTheilsUStatisticCalculator calculator = new OnlineTheilsUStatisticCalculator();
+
+      // always move forward all enumerators (do not use short-circuit evaluation!)
+      while (startValueEnumerator.MoveNext() & actualContinuationsEnumerator.MoveNext() & referenceContinuationsEnumerator.MoveNext() & predictedContinuationsEnumerator.MoveNext()) {
+        calculator.Add(startValueEnumerator.Current, actualContinuationsEnumerator.Current, referenceContinuationsEnumerator.Current, predictedContinuationsEnumerator.Current);
+        if (calculator.ErrorState != OnlineCalculatorError.None) break;
+      }
+
+      // check if all enumerators are at the end to make sure both enumerations have the same length
+      if (calculator.ErrorState == OnlineCalculatorError.None &&
+          (startValueEnumerator.MoveNext() || actualContinuationsEnumerator.MoveNext() || referenceContinuationsEnumerator.MoveNext() || predictedContinuationsEnumerator.MoveNext())) {
+        throw new ArgumentException("Number of elements in startValues, actualContinuations, referenceContinuation and estimatedValues predictedContinuations doesn't match.");
+      } else {
+        errorState = calculator.ErrorState;
+        return calculator.TheilsUStatistic;
+      }
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineWeightedDirectionalSymmetryCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineWeightedDirectionalSymmetryCalculator.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/OnlineWeightedDirectionalSymmetryCalculator.cs	(revision 8811)
@@ -0,0 +1,121 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
+using System.Collections.Generic;
+using HeuristicLab.Common;
+
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class OnlineWeightedDirectionalSymmetryCalculator : IOnlineTimeSeriesCalculator {
+    private int n;
+    private double correctSum;
+    private double incorrectSum;
+
+    public double WeightedDirectionalSymmetry {
+      get {
+        if (n <= 1) return 0.0;
+        return incorrectSum / correctSum;
+      }
+    }
+
+    public OnlineWeightedDirectionalSymmetryCalculator() {
+      Reset();
+    }
+
+    public double Value {
+      get { return WeightedDirectionalSymmetry; }
+    }
+
+    private OnlineCalculatorError errorState;
+    public OnlineCalculatorError ErrorState {
+      get { return errorState; }
+    }
+
+    public void Add(double startValue, IEnumerable<double> actualContinuation, IEnumerable<double> predictedContinuation) {
+      if (double.IsNaN(startValue) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0) {
+        errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+      } else {
+        var actualEnumerator = actualContinuation.GetEnumerator();
+        var predictedEnumerator = predictedContinuation.GetEnumerator();
+        while (actualEnumerator.MoveNext() & predictedEnumerator.MoveNext() & errorState != OnlineCalculatorError.InvalidValueAdded) {
+          double actual = actualEnumerator.Current;
+          double predicted = predictedEnumerator.Current;
+          if (double.IsNaN(actual) || double.IsNaN(predicted)) {
+            errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+          } else {
+            double err = Math.Abs(actual - predicted);
+            // count as correct only if the trend (positive/negative/no change) is predicted correctly
+            if ((actual - startValue) * (predicted - startValue) > 0.0 ||
+              (actual - startValue).IsAlmost(predicted - startValue)) {
+              correctSum += err;
+            } else {
+              incorrectSum += err;
+            }
+            n++;
+          }
+        }
+        // check if both enumerators are at the end to make sure both enumerations have the same length
+        if (actualEnumerator.MoveNext() || predictedEnumerator.MoveNext()) {
+          errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
+        } else {
+          errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded); // n >= 1
+        }
+      }
+    }
+
+    public void Reset() {
+      n = 0;
+      correctSum = 0;
+      incorrectSum = 0;
+      errorState = OnlineCalculatorError.InsufficientElementsAdded;
+    }
+
+    public static double Calculate(double startValue, IEnumerable<double> actualContinuation, IEnumerable<double> predictedContinuation, out OnlineCalculatorError errorState) {
+      OnlineWeightedDirectionalSymmetryCalculator calculator = new OnlineWeightedDirectionalSymmetryCalculator();
+      calculator.Add(startValue, actualContinuation, predictedContinuation);
+      errorState = calculator.ErrorState;
+      return calculator.WeightedDirectionalSymmetry;
+    }
+
+    public static double Calculate(IEnumerable<double> startValues, IEnumerable<IEnumerable<double>> actualContinuations, IEnumerable<IEnumerable<double>> predictedContinuations, out OnlineCalculatorError errorState) {
+      IEnumerator<double> startValueEnumerator = startValues.GetEnumerator();
+      IEnumerator<IEnumerable<double>> actualContinuationsEnumerator = actualContinuations.GetEnumerator();
+      IEnumerator<IEnumerable<double>> predictedContinuationsEnumerator = predictedContinuations.GetEnumerator();
+      OnlineWeightedDirectionalSymmetryCalculator calculator = new OnlineWeightedDirectionalSymmetryCalculator();
+
+      // always move forward all enumerators (do not use short-circuit evaluation!)
+      while (startValueEnumerator.MoveNext() & actualContinuationsEnumerator.MoveNext() & predictedContinuationsEnumerator.MoveNext()) {
+        calculator.Add(startValueEnumerator.Current, actualContinuationsEnumerator.Current, predictedContinuationsEnumerator.Current);
+        if (calculator.ErrorState != OnlineCalculatorError.None) break;
+      }
+
+      // check if all enumerators are at the end to make sure both enumerations have the same length
+      if (calculator.ErrorState == OnlineCalculatorError.None &&
+          (startValueEnumerator.MoveNext() || actualContinuationsEnumerator.MoveNext() || predictedContinuationsEnumerator.MoveNext())) {
+        throw new ArgumentException("Number of elements in startValues, actualContinuations and estimatedValues predictedContinuations doesn't match.");
+      } else {
+        errorState = calculator.ErrorState;
+        return calculator.WeightedDirectionalSymmetry;
+      }
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/SpearmansRankCorrelationCoefficientCalculator.cs
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/SpearmansRankCorrelationCoefficientCalculator.cs	(revision 8811)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/OnlineCalculators/SpearmansRankCorrelationCoefficientCalculator.cs	(revision 8811)
@@ -0,0 +1,43 @@
+﻿#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2012 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System.Collections.Generic;
+using System.Linq;
+
+namespace HeuristicLab.Problems.DataAnalysis {
+  public class SpearmansRankCorrelationCoefficientCalculator {
+
+    public static double Calculate(IEnumerable<double> originalValues, IEnumerable<double> estimatedValues, out OnlineCalculatorError errorState) {
+      double rs = double.NaN;
+      try {
+        var original = originalValues.ToArray();
+        var estimated = estimatedValues.ToArray();
+        rs = alglib.basestat.spearmancorr2(original, estimated, original.Length);
+        errorState = OnlineCalculatorError.None;
+      }
+      catch (alglib.alglibexception) {
+        errorState = OnlineCalculatorError.InvalidValueAdded;
+      }
+
+      return rs;
+    }
+  }
+}
Index: /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Plugin.cs.frame
===================================================================
--- /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Plugin.cs.frame	(revision 8810)
+++ /branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Plugin.cs.frame	(revision 8811)
@@ -28,4 +28,5 @@
   [Plugin("HeuristicLab.Problems.DataAnalysis","Provides base classes for data analysis tasks.", "3.4.3.$WCREV$")]
   [PluginFile("HeuristicLab.Problems.DataAnalysis-3.4.dll", PluginFileType.Assembly)]
+  [PluginDependency("HeuristicLab.ALGLIB","3.6")]
   [PluginDependency("HeuristicLab.Collections", "3.3")]
   [PluginDependency("HeuristicLab.Common", "3.3")]
