source: branches/HeuristicLab.TimeSeries/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs @ 8468

#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.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 TrainingTheilsUStatisticMovingAverageResultName = "Theil's U2 (moving average) (training)";
    protected const string TestTheilsUStatisticMovingAverageResultName = "Theil's U2 (moving average) (test)";

    protected const string PrognosisTrainingMeanSquaredErrorResultName = "Prognosis " + TrainingMeanSquaredErrorResultName;
    protected const string PrognosisTestMeanSquaredErrorResultName = "Prognosis " + TestMeanSquaredErrorResultName;
    protected const string PrognosisTrainingMeanAbsoluteErrorResultName = "Prognosis " + TrainingMeanAbsoluteErrorResultName;
    protected const string PrognosisTestMeanAbsoluteErrorResultName = "Prognosis " + TestMeanAbsoluteErrorResultName;
    protected const string PrognosisTrainingSquaredCorrelationResultName = "Prognosis " + TrainingSquaredCorrelationResultName;
    protected const string PrognosisTestSquaredCorrelationResultName = "Prognosis " + TestSquaredCorrelationResultName;
    protected const string PrognosisTrainingRelativeErrorResultName = "Prognosis " + TrainingRelativeErrorResultName;
    protected const string PrognosisTestRelativeErrorResultName = "Prognosis " + TestRelativeErrorResultName;
    protected const string PrognosisTrainingNormalizedMeanSquaredErrorResultName = "Prognosis " + TrainingNormalizedMeanSquaredErrorResultName;
    protected const string PrognosisTestNormalizedMeanSquaredErrorResultName = "Prognosis " + TestNormalizedMeanSquaredErrorResultName;
    protected const string PrognosisTrainingMeanErrorResultName = "Prognosis " + TrainingMeanErrorResultName;
    protected const string PrognosisTestMeanErrorResultName = "Prognosis " + TestMeanErrorResultName;

    protected const string PrognosisTrainingDirectionalSymmetryResultName = "Prognosis " + TrainingDirectionalSymmetryResultName;
    protected const string PrognosisTestDirectionalSymmetryResultName = "Prognosis " + TestDirectionalSymmetryResultName;
    protected const string PrognosisTrainingWeightedDirectionalSymmetryResultName = "Prognosis " + TrainingWeightedDirectionalSymmetryResultName;
    protected const string PrognosisTestWeightedDirectionalSymmetryResultName = "Prognosis " + TestWeightedDirectionalSymmetryResultName;
    protected const string PrognosisTrainingTheilsUStatisticAR1ResultName = "Prognosis " + TrainingTheilsUStatisticAR1ResultName;
    protected const string PrognosisTestTheilsUStatisticAR1ResultName = "Prognosis " + TestTheilsUStatisticLastResultName;
    protected const string PrognosisTrainingTheilsUStatisticMeanResultName = "Prognosis " + TrainingTheilsUStatisticMeanResultName;
    protected const string PrognosisTestTheilsUStatisticMeanResultName = "Prognosis " + TestTheilsUStatisticMeanResultName;
    protected const string PrognosisTrainingTheilsUStatisticMovingAverageResultName = "Prognosis " + TrainingTheilsUStatisticMovingAverageResultName;
    protected const string PrognosisTestTheilsUStatisticMovingAverageResultName = "Prognosis " + TestTheilsUStatisticMovingAverageResultName;
    #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 TrainingTheilsUStatisticMovingAverageResultDescription = "The Theil's U statistic (reference: moving average model) of the forecasts of the model on the training partition";
    protected const string TestTheilsUStatisticMovingAverageResultDescription = "The Theil's U statistic (reference: moving average model) of the forecasts of the model on the test partition";

    protected const string PrognosisTrainingMeanSquaredErrorResultDescription = TrainingMeanSquaredErrorResultDescription;
    protected const string PrognosisTestMeanSquaredErrorResultDescription = TestMeanSquaredErrorResultDescription;
    protected const string PrognosisTrainingMeanAbsoluteErrorResultDescription = TrainingMeanAbsoluteErrorResultDescription;
    protected const string PrognosisTestMeanAbsoluteErrorResultDescription = TestMeanAbsoluteErrorResultDescription;
    protected const string PrognosisTrainingSquaredCorrelationResultDescription = TrainingSquaredCorrelationResultDescription;
    protected const string PrognosisTestSquaredCorrelationResultDescription = TestSquaredCorrelationResultDescription;
    protected const string PrognosisTrainingRelativeErrorResultDescription = TrainingRelativeErrorResultDescription;
    protected const string PrognosisTestRelativeErrorResultDescription = TestRelativeErrorResultDescription;
    protected const string PrognosisTrainingNormalizedMeanSquaredErrorResultDescription = TrainingNormalizedMeanSquaredErrorResultDescription;
    protected const string PrognosisTestNormalizedMeanSquaredErrorResultDescription = TestNormalizedMeanSquaredErrorResultDescription;
    protected const string PrognosisTrainingMeanErrorResultDescription = TrainingMeanErrorResultDescription;
    protected const string PrognosisTestMeanErrorResultDescription = TestMeanErrorResultDescription;

    protected const string PrognosisTrainingDirectionalSymmetryResultDescription = TrainingDirectionalSymmetryResultDescription;
    protected const string PrognosisTestDirectionalSymmetryResultDescription = TestDirectionalSymmetryResultDescription;
    protected const string PrognosisTrainingWeightedDirectionalSymmetryResultDescription = TrainingWeightedDirectionalSymmetryResultDescription;
    protected const string PrognosisTestWeightedDirectionalSymmetryResultDescription = TestWeightedDirectionalSymmetryResultDescription;
    protected const string PrognosisTrainingTheilsUStatisticAR1ResultDescription = TrainingTheilsUStatisticAR1ResultDescription;
    protected const string PrognosisTestTheilsUStatisticAR1ResultDescription = TestTheilsUStatisticAR1ResultDescription;
    protected const string PrognosisTrainingTheilsUStatisticMeanResultDescription = TrainingTheilsUStatisticMeanResultDescription;
    protected const string PrognosisTestTheilsUStatisticMeanResultDescription = TestTheilsUStatisticMeanResultDescription;
    protected const string PrognosisTrainingTheilsUStatisticMovingAverageResultDescription = TrainingTheilsUStatisticMovingAverageResultDescription;
    protected const string PrognosisTestTheilsUStatisticMovingAverageResultDescription = TestTheilsUStatisticMovingAverageResultDescription;
    #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 double TrainingTheilsUStatisticMovingAverage {
      get { return ((DoubleValue)this[TrainingTheilsUStatisticMovingAverageResultName].Value).Value; }
      private set { ((DoubleValue)this[TrainingTheilsUStatisticMovingAverageResultName].Value).Value = value; }
    }
    public double TestTheilsUStatisticMovingAverage {
      get { return ((DoubleValue)this[TestTheilsUStatisticMovingAverageResultName].Value).Value; }
      private set { ((DoubleValue)this[TestTheilsUStatisticMovingAverageResultName].Value).Value = value; }
    }

    //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;
      }
    }
    public double PrognosisTrainingTheilsUStatisticMovingAverage {
      get {
        if (!ContainsKey(PrognosisTrainingTheilsUStatisticMovingAverageResultName)) return double.NaN;
        return ((DoubleValue)this[PrognosisTrainingTheilsUStatisticMovingAverageResultName].Value).Value;
      }
      private set {
        if (!ContainsKey(PrognosisTrainingTheilsUStatisticMovingAverageResultName)) Add(new Result(PrognosisTrainingTheilsUStatisticMovingAverageResultName, PrognosisTrainingTheilsUStatisticMovingAverageResultDescription, new DoubleValue()));
        ((DoubleValue)this[PrognosisTrainingTheilsUStatisticMovingAverageResultName].Value).Value = value;
      }
    }
    public double PrognosisTestTheilsUStatisticMovingAverage {
      get {
        if (!ContainsKey(PrognosisTestTheilsUStatisticMovingAverageResultName)) return double.NaN;
        return ((DoubleValue)this[PrognosisTestTheilsUStatisticMovingAverageResultName].Value).Value;
      }
      private set {
        if (!ContainsKey(PrognosisTestTheilsUStatisticMovingAverageResultName)) Add(new Result(PrognosisTestTheilsUStatisticMovingAverageResultName, PrognosisTestTheilsUStatisticMovingAverageResultDescription, new DoubleValue()));
        ((DoubleValue)this[PrognosisTestTheilsUStatisticMovingAverageResultName].Value).Value = 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()));
      Add(new Result(TrainingTheilsUStatisticMovingAverageResultName, TrainingTheilsUStatisticMovingAverageResultDescription, new DoubleValue()));
      Add(new Result(TestTheilsUStatisticMovingAverageResultName, TestTheilsUStatisticMovingAverageResultDescription, new DoubleValue()));
    }

    protected override void RecalculateResults() {
      base.RecalculateResults();
      CalculateTimeSeriesResults();
      CalculateTimeSeriesResults(ProblemData.TrainingHorizon, ProblemData.TestHorizon);
    }

    private 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);

      //MA model
      const int movingAverageWindowSize = 10;
      var movingAverageModel = new TimeSeriesPrognosisMovingAverageModel(movingAverageWindowSize, ProblemData.TargetVariable);

      #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();
      IEnumerable<double> trainingMovingAverageModelPredictions = movingAverageModel.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;
      TrainingTheilsUStatisticMovingAverage = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingMovingAverageModelPredictions, trainingEstimatedValues, out errorState);
      TrainingTheilsUStatisticMovingAverage = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMovingAverage : 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();
      IEnumerable<double> testMovingAverageModelPredictions = movingAverageModel.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;
      TestTheilsUStatisticMovingAverage = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testMovingAverageModelPredictions, testEstimatedValues, out errorState);
      TestTheilsUStatisticMovingAverage = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMovingAverage : double.PositiveInfinity;
      #endregion
    }

    private void CalculateTimeSeriesResults(int trainingHorizon, int testHorizon) {
      OnlineCalculatorError errorState;
      //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);

      //MA model
      const int movingAverageWindowSize = 10;
      var MovingAverageModel = new TimeSeriesPrognosisMovingAverageModel(movingAverageWindowSize, ProblemData.TargetVariable);

      #region Calculate training quality measures
      if (trainingHorizon != 1) {
        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<IEnumerable<double>> trainingMovingAverageModelPredictions = MovingAverageModel.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;
        PrognosisTrainingTheilsUStatisticMovingAverage = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMovingAverageModelPredictions, trainingEstimatedValues, out errorState);
        PrognosisTrainingTheilsUStatisticMovingAverage = errorState == OnlineCalculatorError.None ? PrognosisTrainingTheilsUStatisticMovingAverage : double.PositiveInfinity;
      }

      #endregion

      #region  Calculate test quality measures
      if (testHorizon != 1) {
        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<IEnumerable<double>> testMovingAverageModelPredictions = MovingAverageModel.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;
        PrognosisTestTheilsUStatisticMovingAverage = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMovingAverageModelPredictions, testEstimatedValues, out errorState);
        PrognosisTestTheilsUStatisticMovingAverage = errorState == OnlineCalculatorError.None ? PrognosisTestTheilsUStatisticMovingAverage : double.PositiveInfinity;
      }

      #endregion
    }
  }
}