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Timestamp:
10/05/12 17:57:54 (12 years ago)
Author:
mkommend
Message:

#1081: Extracted prognosis results into separate class and added a view for them.

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1 edited

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  • branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs

    r8742 r8750  
    2020#endregion
    2121
    22 using System;
    2322using System.Collections.Generic;
    2423using System.Linq;
    … …  
    4241    protected const string TrainingTheilsUStatisticMovingAverageResultName = "Theil's U2 (moving average) (training)";
    4342    protected const string TestTheilsUStatisticMovingAverageResultName = "Theil's U2 (moving average) (test)";
    44 
    45     protected const string PrognosisTrainingMeanSquaredErrorResultName = "Prognosis " + TrainingMeanSquaredErrorResultName;
    46     protected const string PrognosisTestMeanSquaredErrorResultName = "Prognosis " + TestMeanSquaredErrorResultName;
    47     protected const string PrognosisTrainingMeanAbsoluteErrorResultName = "Prognosis " + TrainingMeanAbsoluteErrorResultName;
    48     protected const string PrognosisTestMeanAbsoluteErrorResultName = "Prognosis " + TestMeanAbsoluteErrorResultName;
    49     protected const string PrognosisTrainingSquaredCorrelationResultName = "Prognosis " + TrainingSquaredCorrelationResultName;
    50     protected const string PrognosisTestSquaredCorrelationResultName = "Prognosis " + TestSquaredCorrelationResultName;
    51     protected const string PrognosisTrainingRelativeErrorResultName = "Prognosis " + TrainingRelativeErrorResultName;
    52     protected const string PrognosisTestRelativeErrorResultName = "Prognosis " + TestRelativeErrorResultName;
    53     protected const string PrognosisTrainingNormalizedMeanSquaredErrorResultName = "Prognosis " + TrainingNormalizedMeanSquaredErrorResultName;
    54     protected const string PrognosisTestNormalizedMeanSquaredErrorResultName = "Prognosis " + TestNormalizedMeanSquaredErrorResultName;
    55     protected const string PrognosisTrainingMeanErrorResultName = "Prognosis " + TrainingMeanErrorResultName;
    56     protected const string PrognosisTestMeanErrorResultName = "Prognosis " + TestMeanErrorResultName;
    57 
    58     protected const string PrognosisTrainingDirectionalSymmetryResultName = "Prognosis " + TrainingDirectionalSymmetryResultName;
    59     protected const string PrognosisTestDirectionalSymmetryResultName = "Prognosis " + TestDirectionalSymmetryResultName;
    60     protected const string PrognosisTrainingWeightedDirectionalSymmetryResultName = "Prognosis " + TrainingWeightedDirectionalSymmetryResultName;
    61     protected const string PrognosisTestWeightedDirectionalSymmetryResultName = "Prognosis " + TestWeightedDirectionalSymmetryResultName;
    62     protected const string PrognosisTrainingTheilsUStatisticAR1ResultName = "Prognosis " + TrainingTheilsUStatisticAR1ResultName;
    63     protected const string PrognosisTestTheilsUStatisticAR1ResultName = "Prognosis " + TestTheilsUStatisticLastResultName;
    64     protected const string PrognosisTrainingTheilsUStatisticMeanResultName = "Prognosis " + TrainingTheilsUStatisticMeanResultName;
    65     protected const string PrognosisTestTheilsUStatisticMeanResultName = "Prognosis " + TestTheilsUStatisticMeanResultName;
    66     protected const string PrognosisTrainingTheilsUStatisticMovingAverageResultName = "Prognosis " + TrainingTheilsUStatisticMovingAverageResultName;
    67     protected const string PrognosisTestTheilsUStatisticMovingAverageResultName = "Prognosis " + TestTheilsUStatisticMovingAverageResultName;
     43    protected const string TimeSeriesPrognosisResultName = "Prognosis Results";
    6844    #endregion
    6945
    … …  
    7955    protected const string TrainingTheilsUStatisticMovingAverageResultDescription = "The Theil's U statistic (reference: moving average model) of the forecasts of the model on the training partition";
    8056    protected const string TestTheilsUStatisticMovingAverageResultDescription = "The Theil's U statistic (reference: moving average model) of the forecasts of the model on the test partition";
    81 
    82     protected const string PrognosisTrainingMeanSquaredErrorResultDescription = TrainingMeanSquaredErrorResultDescription;
    83     protected const string PrognosisTestMeanSquaredErrorResultDescription = TestMeanSquaredErrorResultDescription;
    84     protected const string PrognosisTrainingMeanAbsoluteErrorResultDescription = TrainingMeanAbsoluteErrorResultDescription;
    85     protected const string PrognosisTestMeanAbsoluteErrorResultDescription = TestMeanAbsoluteErrorResultDescription;
    86     protected const string PrognosisTrainingSquaredCorrelationResultDescription = TrainingSquaredCorrelationResultDescription;
    87     protected const string PrognosisTestSquaredCorrelationResultDescription = TestSquaredCorrelationResultDescription;
    88     protected const string PrognosisTrainingRelativeErrorResultDescription = TrainingRelativeErrorResultDescription;
    89     protected const string PrognosisTestRelativeErrorResultDescription = TestRelativeErrorResultDescription;
    90     protected const string PrognosisTrainingNormalizedMeanSquaredErrorResultDescription = TrainingNormalizedMeanSquaredErrorResultDescription;
    91     protected const string PrognosisTestNormalizedMeanSquaredErrorResultDescription = TestNormalizedMeanSquaredErrorResultDescription;
    92     protected const string PrognosisTrainingMeanErrorResultDescription = TrainingMeanErrorResultDescription;
    93     protected const string PrognosisTestMeanErrorResultDescription = TestMeanErrorResultDescription;
    94 
    95     protected const string PrognosisTrainingDirectionalSymmetryResultDescription = TrainingDirectionalSymmetryResultDescription;
    96     protected const string PrognosisTestDirectionalSymmetryResultDescription = TestDirectionalSymmetryResultDescription;
    97     protected const string PrognosisTrainingWeightedDirectionalSymmetryResultDescription = TrainingWeightedDirectionalSymmetryResultDescription;
    98     protected const string PrognosisTestWeightedDirectionalSymmetryResultDescription = TestWeightedDirectionalSymmetryResultDescription;
    99     protected const string PrognosisTrainingTheilsUStatisticAR1ResultDescription = TrainingTheilsUStatisticAR1ResultDescription;
    100     protected const string PrognosisTestTheilsUStatisticAR1ResultDescription = TestTheilsUStatisticAR1ResultDescription;
    101     protected const string PrognosisTrainingTheilsUStatisticMeanResultDescription = TrainingTheilsUStatisticMeanResultDescription;
    102     protected const string PrognosisTestTheilsUStatisticMeanResultDescription = TestTheilsUStatisticMeanResultDescription;
    103     protected const string PrognosisTrainingTheilsUStatisticMovingAverageResultDescription = TrainingTheilsUStatisticMovingAverageResultDescription;
    104     protected const string PrognosisTestTheilsUStatisticMovingAverageResultDescription = TestTheilsUStatisticMovingAverageResultDescription;
     57    protected const string TimeSeriesPrognosisResultDescription = "The calculated results of predictions in the future.";
    10558    #endregion
    10659
    … …  
    159112    }
    160113
    161     //prognosis results for different horizons
    162     public double PrognosisTrainingMeanSquaredError {
     114    public TimeSeriesPrognosisResults TimeSeriesPrognosisResults {
    163115      get {
    164         if (!ContainsKey(PrognosisTrainingMeanSquaredErrorResultName)) return double.NaN;
    165         return ((DoubleValue)this[PrognosisTrainingMeanSquaredErrorResultName].Value).Value;
     116        if (!ContainsKey(TimeSeriesPrognosisResultName)) return null;
     117        return (TimeSeriesPrognosisResults)this[TimeSeriesPrognosisResultName];
    166118      }
    167       private set {
    168         if (!ContainsKey(PrognosisTrainingMeanSquaredErrorResultName)) Add(new Result(PrognosisTrainingMeanSquaredErrorResultName, PrognosisTrainingMeanSquaredErrorResultDescription, new DoubleValue()));
    169         ((DoubleValue)this[PrognosisTrainingMeanSquaredErrorResultName].Value).Value = value;
     119      set {
     120        if (ContainsKey(TimeSeriesPrognosisResultName)) Remove(TimeSeriesPrognosisResultName);
     121        Add(new Result(TimeSeriesPrognosisResultName, TimeSeriesPrognosisResultDescription, value));
    170122      }
    171123    }
    172 
    173     public double PrognosisTestMeanSquaredError {
    174       get {
    175         if (!ContainsKey(PrognosisTestMeanSquaredErrorResultName)) return double.NaN;
    176         return ((DoubleValue)this[PrognosisTestMeanSquaredErrorResultName].Value).Value;
    177       }
    178       private set {
    179         if (!ContainsKey(PrognosisTestMeanSquaredErrorResultName)) Add(new Result(PrognosisTestMeanSquaredErrorResultName, PrognosisTestMeanSquaredErrorResultDescription, new DoubleValue()));
    180         ((DoubleValue)this[PrognosisTestMeanSquaredErrorResultName].Value).Value = value;
    181       }
    182     }
    183 
    184     public double PrognosisTrainingMeanAbsoluteError {
    185       get {
    186         if (!ContainsKey(PrognosisTrainingMeanAbsoluteErrorResultName)) return double.NaN;
    187         return ((DoubleValue)this[PrognosisTrainingMeanAbsoluteErrorResultName].Value).Value;
    188       }
    189       private set {
    190         if (!ContainsKey(PrognosisTrainingMeanAbsoluteErrorResultName)) Add(new Result(PrognosisTrainingMeanAbsoluteErrorResultName, PrognosisTrainingMeanAbsoluteErrorResultDescription, new DoubleValue()));
    191         ((DoubleValue)this[PrognosisTrainingMeanAbsoluteErrorResultName].Value).Value = value;
    192       }
    193     }
    194 
    195     public double PrognosisTestMeanAbsoluteError {
    196       get {
    197         if (!ContainsKey(PrognosisTestMeanAbsoluteErrorResultName)) return double.NaN;
    198         return ((DoubleValue)this[PrognosisTestMeanAbsoluteErrorResultName].Value).Value;
    199       }
    200       private set {
    201         if (!ContainsKey(PrognosisTestMeanAbsoluteErrorResultName)) Add(new Result(PrognosisTestMeanAbsoluteErrorResultName, PrognosisTestMeanAbsoluteErrorResultDescription, new DoubleValue()));
    202         ((DoubleValue)this[PrognosisTestMeanAbsoluteErrorResultName].Value).Value = value;
    203       }
    204     }
    205 
    206     public double PrognosisTrainingRSquared {
    207       get {
    208         if (!ContainsKey(PrognosisTrainingSquaredCorrelationResultName)) return double.NaN;
    209         return ((DoubleValue)this[PrognosisTrainingSquaredCorrelationResultName].Value).Value;
    210       }
    211       private set {
    212         if (!ContainsKey(PrognosisTrainingSquaredCorrelationResultName)) Add(new Result(PrognosisTrainingSquaredCorrelationResultName, PrognosisTrainingSquaredCorrelationResultDescription, new DoubleValue()));
    213         ((DoubleValue)this[PrognosisTrainingSquaredCorrelationResultName].Value).Value = value;
    214       }
    215     }
    216 
    217     public double PrognosisTestRSquared {
    218       get {
    219         if (!ContainsKey(PrognosisTestSquaredCorrelationResultName)) return double.NaN;
    220         return ((DoubleValue)this[PrognosisTestSquaredCorrelationResultName].Value).Value;
    221       }
    222       private set {
    223         if (!ContainsKey(PrognosisTestSquaredCorrelationResultName)) Add(new Result(PrognosisTestSquaredCorrelationResultName, PrognosisTestSquaredCorrelationResultDescription, new DoubleValue()));
    224         ((DoubleValue)this[PrognosisTestSquaredCorrelationResultName].Value).Value = value;
    225       }
    226     }
    227 
    228     public double PrognosisTrainingRelativeError {
    229       get {
    230         if (!ContainsKey(PrognosisTrainingRelativeErrorResultName)) return double.NaN;
    231         return ((DoubleValue)this[PrognosisTrainingRelativeErrorResultName].Value).Value;
    232       }
    233       private set {
    234         if (!ContainsKey(PrognosisTrainingRelativeErrorResultName)) Add(new Result(PrognosisTrainingRelativeErrorResultName, PrognosisTrainingRelativeErrorResultDescription, new DoubleValue()));
    235         ((DoubleValue)this[PrognosisTrainingRelativeErrorResultName].Value).Value = value;
    236       }
    237     }
    238 
    239     public double PrognosisTestRelativeError {
    240       get {
    241         if (!ContainsKey(PrognosisTestRelativeErrorResultName)) return double.NaN;
    242         return ((DoubleValue)this[PrognosisTestRelativeErrorResultName].Value).Value;
    243       }
    244       private set {
    245         if (!ContainsKey(PrognosisTestRelativeErrorResultName)) Add(new Result(PrognosisTestRelativeErrorResultName, PrognosisTestRelativeErrorResultDescription, new DoubleValue()));
    246         ((DoubleValue)this[PrognosisTestRelativeErrorResultName].Value).Value = value;
    247       }
    248     }
    249 
    250     public double PrognosisTrainingNormalizedMeanSquaredError {
    251       get {
    252         if (!ContainsKey(PrognosisTrainingNormalizedMeanSquaredErrorResultName)) return double.NaN;
    253         return ((DoubleValue)this[PrognosisTrainingNormalizedMeanSquaredErrorResultName].Value).Value;
    254       }
    255       private set {
    256         if (!ContainsKey(PrognosisTrainingNormalizedMeanSquaredErrorResultName)) Add(new Result(PrognosisTrainingNormalizedMeanSquaredErrorResultName, PrognosisTrainingNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
    257         ((DoubleValue)this[PrognosisTrainingNormalizedMeanSquaredErrorResultName].Value).Value = value;
    258       }
    259     }
    260 
    261     public double PrognosisTestNormalizedMeanSquaredError {
    262       get {
    263         if (!ContainsKey(PrognosisTestNormalizedMeanSquaredErrorResultName)) return double.NaN;
    264         return ((DoubleValue)this[PrognosisTestNormalizedMeanSquaredErrorResultName].Value).Value;
    265       }
    266       private set {
    267         if (!ContainsKey(PrognosisTestNormalizedMeanSquaredErrorResultName)) Add(new Result(PrognosisTestNormalizedMeanSquaredErrorResultName, PrognosisTestNormalizedMeanSquaredErrorResultDescription, new DoubleValue()));
    268         ((DoubleValue)this[PrognosisTestNormalizedMeanSquaredErrorResultName].Value).Value = value;
    269       }
    270     }
    271 
    272     public double PrognosisTrainingMeanError {
    273       get {
    274         if (!ContainsKey(PrognosisTrainingMeanErrorResultName)) return double.NaN;
    275         return ((DoubleValue)this[PrognosisTrainingMeanErrorResultName].Value).Value;
    276       }
    277       private set {
    278         if (!ContainsKey(PrognosisTrainingMeanErrorResultName)) Add(new Result(PrognosisTrainingMeanErrorResultName, PrognosisTrainingMeanErrorResultDescription, new DoubleValue()));
    279         ((DoubleValue)this[PrognosisTrainingMeanErrorResultName].Value).Value = value;
    280       }
    281     }
    282 
    283     public double PrognosisTestMeanError {
    284       get {
    285         if (!ContainsKey(PrognosisTestMeanErrorResultName)) return double.NaN;
    286         return ((DoubleValue)this[PrognosisTestMeanErrorResultName].Value).Value;
    287       }
    288       private set {
    289         if (!ContainsKey(PrognosisTestMeanErrorResultName)) Add(new Result(PrognosisTestMeanErrorResultName, PrognosisTestMeanErrorResultDescription, new DoubleValue()));
    290         ((DoubleValue)this[PrognosisTestMeanErrorResultName].Value).Value = value;
    291       }
    292     }
    293 
    294 
    295     public double PrognosisTrainingDirectionalSymmetry {
    296       get {
    297         if (!ContainsKey(PrognosisTrainingDirectionalSymmetryResultName)) return double.NaN;
    298         return ((DoubleValue)this[PrognosisTrainingDirectionalSymmetryResultName].Value).Value;
    299       }
    300       private set {
    301         if (!ContainsKey(PrognosisTrainingDirectionalSymmetryResultName)) Add(new Result(PrognosisTrainingDirectionalSymmetryResultName, PrognosisTrainingDirectionalSymmetryResultDescription, new DoubleValue()));
    302         ((DoubleValue)this[PrognosisTrainingDirectionalSymmetryResultName].Value).Value = value;
    303       }
    304     }
    305     public double PrognosisTestDirectionalSymmetry {
    306       get {
    307         if (!ContainsKey(PrognosisTestDirectionalSymmetryResultName)) return double.NaN;
    308         return ((DoubleValue)this[PrognosisTestDirectionalSymmetryResultName].Value).Value;
    309       }
    310       private set {
    311         if (!ContainsKey(PrognosisTestDirectionalSymmetryResultName)) Add(new Result(PrognosisTestDirectionalSymmetryResultName, PrognosisTestDirectionalSymmetryResultDescription, new DoubleValue()));
    312         ((DoubleValue)this[PrognosisTestDirectionalSymmetryResultName].Value).Value = value;
    313       }
    314     }
    315     public double PrognosisTrainingWeightedDirectionalSymmetry {
    316       get {
    317         if (!ContainsKey(PrognosisTrainingWeightedDirectionalSymmetryResultName)) return double.NaN;
    318         return ((DoubleValue)this[PrognosisTrainingWeightedDirectionalSymmetryResultName].Value).Value;
    319       }
    320       private set {
    321         if (!ContainsKey(PrognosisTrainingWeightedDirectionalSymmetryResultName)) Add(new Result(PrognosisTrainingWeightedDirectionalSymmetryResultName, PrognosisTrainingWeightedDirectionalSymmetryResultDescription, new DoubleValue()));
    322         ((DoubleValue)this[PrognosisTrainingWeightedDirectionalSymmetryResultName].Value).Value = value;
    323       }
    324     }
    325     public double PrognosisTestWeightedDirectionalSymmetry {
    326       get {
    327         if (!ContainsKey(PrognosisTestWeightedDirectionalSymmetryResultName)) return double.NaN;
    328         return ((DoubleValue)this[PrognosisTestWeightedDirectionalSymmetryResultName].Value).Value;
    329       }
    330       private set {
    331         if (!ContainsKey(PrognosisTestWeightedDirectionalSymmetryResultName)) Add(new Result(PrognosisTestWeightedDirectionalSymmetryResultName, PrognosisTestWeightedDirectionalSymmetryResultDescription, new DoubleValue()));
    332         ((DoubleValue)this[PrognosisTestWeightedDirectionalSymmetryResultName].Value).Value = value;
    333       }
    334     }
    335     public double PrognosisTrainingTheilsUStatisticAR1 {
    336       get {
    337         if (!ContainsKey(PrognosisTrainingTheilsUStatisticAR1ResultName)) return double.NaN;
    338         return ((DoubleValue)this[PrognosisTrainingTheilsUStatisticAR1ResultName].Value).Value;
    339       }
    340       private set {
    341         if (!ContainsKey(PrognosisTrainingTheilsUStatisticAR1ResultName)) Add(new Result(PrognosisTrainingTheilsUStatisticAR1ResultName, PrognosisTrainingTheilsUStatisticAR1ResultDescription, new DoubleValue()));
    342         ((DoubleValue)this[PrognosisTrainingTheilsUStatisticAR1ResultName].Value).Value = value;
    343       }
    344     }
    345     public double PrognosisTestTheilsUStatisticAR1 {
    346       get {
    347         if (!ContainsKey(PrognosisTestTheilsUStatisticAR1ResultName)) return double.NaN;
    348         return ((DoubleValue)this[PrognosisTestTheilsUStatisticAR1ResultName].Value).Value;
    349       }
    350       private set {
    351         if (!ContainsKey(PrognosisTestTheilsUStatisticAR1ResultName)) Add(new Result(PrognosisTestTheilsUStatisticAR1ResultName, PrognosisTestTheilsUStatisticAR1ResultDescription, new DoubleValue()));
    352         ((DoubleValue)this[PrognosisTestTheilsUStatisticAR1ResultName].Value).Value = value;
    353       }
    354     }
    355     public double PrognosisTrainingTheilsUStatisticMean {
    356       get {
    357         if (!ContainsKey(PrognosisTrainingTheilsUStatisticMeanResultName)) return double.NaN;
    358         return ((DoubleValue)this[PrognosisTrainingTheilsUStatisticMeanResultName].Value).Value;
    359       }
    360       private set {
    361         if (!ContainsKey(PrognosisTrainingTheilsUStatisticMeanResultName)) Add(new Result(PrognosisTrainingTheilsUStatisticMeanResultName, PrognosisTrainingTheilsUStatisticMeanResultDescription, new DoubleValue()));
    362         ((DoubleValue)this[PrognosisTrainingTheilsUStatisticMeanResultName].Value).Value = value;
    363       }
    364     }
    365     public double PrognosisTestTheilsUStatisticMean {
    366       get {
    367         if (!ContainsKey(PrognosisTestTheilsUStatisticMeanResultName)) return double.NaN;
    368         return ((DoubleValue)this[PrognosisTestTheilsUStatisticMeanResultName].Value).Value;
    369       }
    370       private set {
    371         if (!ContainsKey(PrognosisTestTheilsUStatisticMeanResultName)) Add(new Result(PrognosisTestTheilsUStatisticMeanResultName, PrognosisTestTheilsUStatisticMeanResultDescription, new DoubleValue()));
    372         ((DoubleValue)this[PrognosisTestTheilsUStatisticMeanResultName].Value).Value = value;
    373       }
    374     }
    375     public double PrognosisTrainingTheilsUStatisticMovingAverage {
    376       get {
    377         if (!ContainsKey(PrognosisTrainingTheilsUStatisticMovingAverageResultName)) return double.NaN;
    378         return ((DoubleValue)this[PrognosisTrainingTheilsUStatisticMovingAverageResultName].Value).Value;
    379       }
    380       private set {
    381         if (!ContainsKey(PrognosisTrainingTheilsUStatisticMovingAverageResultName)) Add(new Result(PrognosisTrainingTheilsUStatisticMovingAverageResultName, PrognosisTrainingTheilsUStatisticMovingAverageResultDescription, new DoubleValue()));
    382         ((DoubleValue)this[PrognosisTrainingTheilsUStatisticMovingAverageResultName].Value).Value = value;
    383       }
    384     }
    385     public double PrognosisTestTheilsUStatisticMovingAverage {
    386       get {
    387         if (!ContainsKey(PrognosisTestTheilsUStatisticMovingAverageResultName)) return double.NaN;
    388         return ((DoubleValue)this[PrognosisTestTheilsUStatisticMovingAverageResultName].Value).Value;
    389       }
    390       private set {
    391         if (!ContainsKey(PrognosisTestTheilsUStatisticMovingAverageResultName)) Add(new Result(PrognosisTestTheilsUStatisticMovingAverageResultName, PrognosisTestTheilsUStatisticMovingAverageResultDescription, new DoubleValue()));
    392         ((DoubleValue)this[PrognosisTestTheilsUStatisticMovingAverageResultName].Value).Value = value;
    393       }
    394     }
    395124    #endregion
     125
    396126
    397127    public override IEnumerable<double> EstimatedValues {
    … …  
    485215
    486216    protected void CalculateTimeSeriesResults(int trainingHorizon, int testHorizon) {
    487       OnlineCalculatorError errorState;
    488       //mean model
    489       double trainingMean = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average();
    490       var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
    491 
    492       //AR1 model
    493       double alpha, beta;
    494       IEnumerable<double> trainingStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
    495       OnlineLinearScalingParameterCalculator.Calculate(ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
    496       var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(ProblemData.TargetVariable, new double[] { beta }, alpha);
    497 
    498       //MA model
    499       const int movingAverageWindowSize = 10;
    500       var MovingAverageModel = new TimeSeriesPrognosisMovingAverageModel(movingAverageWindowSize, ProblemData.TargetVariable);
    501 
    502       #region Calculate training quality measures
    503       if (trainingHorizon != 1) {
    504         var trainingHorizions = ProblemData.TrainingIndices.Select(r => Math.Min(trainingHorizon, ProblemData.TrainingPartition.End - r)).ToList();
    505         IEnumerable<IEnumerable<double>> trainingTargetValues = ProblemData.TrainingIndices.Zip(trainingHorizions, Enumerable.Range).Select(r => ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, r)).ToList();
    506         IEnumerable<IEnumerable<double>> trainingEstimatedValues = Model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndices, trainingHorizions).ToList();
    507         IEnumerable<IEnumerable<double>> trainingMeanModelPredictions = meanModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndices, trainingHorizions).ToList();
    508         IEnumerable<IEnumerable<double>> trainingAR1ModelPredictions = AR1model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndices, trainingHorizions).ToList();
    509         IEnumerable<IEnumerable<double>> trainingMovingAverageModelPredictions = MovingAverageModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndices, trainingHorizions).ToList();
    510 
    511         IEnumerable<double> originalTrainingValues = trainingTargetValues.SelectMany(x => x).ToList();
    512         IEnumerable<double> estimatedTrainingValues = trainingEstimatedValues.SelectMany(x => x).ToList();
    513 
    514         double trainingMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    515         PrognosisTrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingMSE : double.NaN;
    516         double trainingMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    517         PrognosisTrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? trainingMAE : double.NaN;
    518         double trainingR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    519         PrognosisTrainingRSquared = errorState == OnlineCalculatorError.None ? trainingR2 : double.NaN;
    520         double trainingRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    521         PrognosisTrainingRelativeError = errorState == OnlineCalculatorError.None ? trainingRelError : double.NaN;
    522         double trainingNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    523         PrognosisTrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? trainingNMSE : double.NaN;
    524         double trainingME = OnlineMeanErrorCalculator.Calculate(originalTrainingValues, estimatedTrainingValues, out errorState);
    525         PrognosisTrainingMeanError = errorState == OnlineCalculatorError.None ? trainingME : double.NaN;
    526 
    527         PrognosisTrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
    528         PrognosisTrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTrainingDirectionalSymmetry : 0.0;
    529         PrognosisTrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
    530         PrognosisTrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTrainingWeightedDirectionalSymmetry : 0.0;
    531         PrognosisTrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
    532         PrognosisTrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticAR1 : double.PositiveInfinity;
    533         PrognosisTrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
    534         PrognosisTrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticMean : double.PositiveInfinity;
    535         PrognosisTrainingTheilsUStatisticMovingAverage = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMovingAverageModelPredictions, trainingEstimatedValues, out errorState);
    536         PrognosisTrainingTheilsUStatisticMovingAverage = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticMovingAverage : double.PositiveInfinity;
    537       }
    538 
    539       #endregion
    540 
    541       #region  Calculate test quality measures
    542       if (testHorizon != 1) {
    543         var testHorizions = ProblemData.TestIndices.Select(r => Math.Min(testHorizon, ProblemData.TestPartition.End - r)).ToList();
    544         IEnumerable<IEnumerable<double>> testTargetValues = ProblemData.TestIndices.Zip(testHorizions, Enumerable.Range).Select(r => ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, r)).ToList();
    545         IEnumerable<IEnumerable<double>> testEstimatedValues = Model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndices, testHorizions).ToList();
    546         IEnumerable<double> testStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices.Select(r => r - 1).Where(r => r > 0)).ToList();
    547         IEnumerable<IEnumerable<double>> testMeanModelPredictions = meanModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndices, testHorizions).ToList();
    548         IEnumerable<IEnumerable<double>> testAR1ModelPredictions = AR1model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndices, testHorizions).ToList();
    549         IEnumerable<IEnumerable<double>> testMovingAverageModelPredictions = MovingAverageModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndices, testHorizions).ToList();
    550 
    551         IEnumerable<double> originalTestValues = testTargetValues.SelectMany(x => x).ToList();
    552         IEnumerable<double> estimatedTestValues = testEstimatedValues.SelectMany(x => x).ToList();
    553 
    554         double testMSE = OnlineMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    555         PrognosisTestMeanSquaredError = errorState == OnlineCalculatorError.None ? testMSE : double.NaN;
    556         double testMAE = OnlineMeanAbsoluteErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    557         PrognosisTestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? testMAE : double.NaN;
    558         double testR2 = OnlinePearsonsRSquaredCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    559         PrognosisTestRSquared = errorState == OnlineCalculatorError.None ? testR2 : double.NaN;
    560         double testRelError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    561         PrognosisTestRelativeError = errorState == OnlineCalculatorError.None ? testRelError : double.NaN;
    562         double testNMSE = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    563         PrognosisTestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? testNMSE : double.NaN;
    564         double testME = OnlineMeanErrorCalculator.Calculate(originalTestValues, estimatedTestValues, out errorState);
    565         PrognosisTestMeanError = errorState == OnlineCalculatorError.None ? testME : double.NaN;
    566 
    567         PrognosisTestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
    568         PrognosisTestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTestDirectionalSymmetry : 0.0;
    569         PrognosisTestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
    570         PrognosisTestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? PrognosisTestWeightedDirectionalSymmetry : 0.0;
    571         PrognosisTestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
    572         PrognosisTestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticAR1 : double.PositiveInfinity;
    573         PrognosisTestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
    574         PrognosisTestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticMean : double.PositiveInfinity;
    575         PrognosisTestTheilsUStatisticMovingAverage = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMovingAverageModelPredictions, testEstimatedValues, out errorState);
    576         PrognosisTestTheilsUStatisticMovingAverage = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticMovingAverage : double.PositiveInfinity;
    577       }
    578 
    579       #endregion
     217      TimeSeriesPrognosisResults = new TimeSeriesPrognosisResults(trainingHorizon, testHorizon, this);
    580218    }
    581219  }
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