Index: branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs
===================================================================
--- branches/ClassificationEnsembleVoting/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Classification/ClassificationEnsembleSolution.cs	(revision 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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 8534)
+++ 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},
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+{9.8419122e-01},
+{1.0332443e+00},
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+{9.9300973e-01},
+{1.1766813e+00},
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+{9.2078170e-01},
+{6.2369710e-01},
+{4.3562308e-01},
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+{1.0183775e+00},
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+{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},
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+{1.1361782e+00},
+{7.9337369e-01},
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+{1.1078273e+00},
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+{7.4186814e-01},
+{6.7554741e-01},
+{8.2771704e-01},
+{1.0970261e+00},
+{1.2115055e+00},
+{1.2926801e+00},
+{1.1044430e+00},
+{7.7268354e-01},
+{5.2367729e-01},
+{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);
+    }
+  }
+}
