Index: /trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs	(revision 13999)
+++ /trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithmStatic.cs	(revision 14000)
@@ -96,5 +96,5 @@
         weights = new List<double>();
         // add constant model
-        models.Add(new ConstantModel(f0));
+        models.Add(new ConstantModel(f0, problemData.TargetVariable));
         weights.Add(1.0);
       }
Index: /trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/RegressionTreeModel.cs
===================================================================
--- /trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/RegressionTreeModel.cs	(revision 13999)
+++ /trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/RegressionTreeModel.cs	(revision 14000)
@@ -160,6 +160,6 @@
     }
 
-    internal RegressionTreeModel(TreeNode[] tree, string target = "Target")
-      : base(target, "RegressionTreeModel", "Represents a decision tree for regression.") {
+    internal RegressionTreeModel(TreeNode[] tree, string targetVariable)
+      : base(targetVariable, "RegressionTreeModel", "Represents a decision tree for regression.") {
       this.tree = tree;
     }
Index: /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/ConstantModel.cs
===================================================================
--- /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/ConstantModel.cs	(revision 13999)
+++ /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/ConstantModel.cs	(revision 14000)
@@ -51,5 +51,5 @@
     public override IDeepCloneable Clone(Cloner cloner) { return new ConstantModel(this, cloner); }
 
-    public ConstantModel(double constant, string targetVariable = "Target")
+    public ConstantModel(double constant, string targetVariable)
       : base(targetVariable) {
       this.name = ItemName;
Index: /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs
===================================================================
--- /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs	(revision 13999)
+++ /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/Regression/ConstantRegressionModel.cs	(revision 14000)
@@ -51,5 +51,5 @@
     public override IDeepCloneable Clone(Cloner cloner) { return new ConstantRegressionModel(this, cloner); }
 
-    public ConstantRegressionModel(double constant, string targetVariable = "Target")
+    public ConstantRegressionModel(double constant, string targetVariable)
       : base(targetVariable) {
       this.name = ItemName;
Index: /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/ConstantTimeSeriesPrognosisModel.cs
===================================================================
--- /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/ConstantTimeSeriesPrognosisModel.cs	(revision 13999)
+++ /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/ConstantTimeSeriesPrognosisModel.cs	(revision 14000)
@@ -39,5 +39,5 @@
     }
 
-    public ConstantTimeSeriesPrognosisModel(double constant, string targetVariable = "Target") : base(constant, targetVariable) { }
+    public ConstantTimeSeriesPrognosisModel(double constant, string targetVariable) : base(constant, targetVariable) { }
 
     public IEnumerable<IEnumerable<double>> GetPrognosedValues(IDataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
Index: /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs
===================================================================
--- /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs	(revision 13999)
+++ /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisResults.cs	(revision 14000)
@@ -373,5 +373,5 @@
       //mean model
       double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
-      var meanModel = new ConstantModel(trainingMean);
+      var meanModel = new ConstantModel(trainingMean, problemData.TargetVariable);
 
       //AR1 model
@@ -395,5 +395,5 @@
       PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
       double trainingR = OnlinePearsonsRCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
-      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR*trainingR : double.NaN;
+      PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR * trainingR : double.NaN;
       double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
       PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
@@ -431,5 +431,5 @@
       PrognosisTestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
       double testR = OnlinePearsonsRCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
-      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR*testR : double.NaN;
+      PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR * testR : double.NaN;
       double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
       PrognosisTestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
@@ -448,5 +448,5 @@
         //mean model
         double trainingMean = problemData.Dataset.GetDoubleValues(problemData.TargetVariable, problemData.TrainingIndices).Average();
-        var meanModel = new ConstantModel(trainingMean);
+        var meanModel = new ConstantModel(trainingMean, problemData.TargetVariable);
 
         //AR1 model
Index: /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs
===================================================================
--- /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs	(revision 13999)
+++ /trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs	(revision 14000)
@@ -150,5 +150,5 @@
       OnlineCalculatorError errorState;
       double trainingMean = ProblemData.TrainingIndices.Any() ? ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average() : double.NaN;
-      var meanModel = new ConstantModel(trainingMean);
+      var meanModel = new ConstantModel(trainingMean,ProblemData.TargetVariable);
 
       double alpha, beta;
