| 1 | #region License Information
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| 2 | /* HeuristicLab
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| 3 | * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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| 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.Linq;
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| 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Data;
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| 27 | using HeuristicLab.Optimization;
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| 28 | using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
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| 29 |
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| 30 | namespace HeuristicLab.Problems.DataAnalysis {
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| 31 | [StorableClass]
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| 32 | public abstract class TimeSeriesPrognosisSolutionBase : DataAnalysisSolution, ITimeSeriesPrognosisSolution {
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| 33 | private const string TrainingMeanSquaredErrorResultName = "Mean squared error (training)";
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| 34 | private const string TestMeanSquaredErrorResultName = "Mean squared error (test)";
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| 35 | private const string TrainingMeanAbsoluteErrorResultName = "Mean absolute error (training)";
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| 36 | private const string TestMeanAbsoluteErrorResultName = "Mean absolute error (test)";
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| 37 | private const string TrainingSquaredCorrelationResultName = "Pearson's R² (training)";
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| 38 | private const string TestSquaredCorrelationResultName = "Pearson's R² (test)";
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| 39 | private const string TrainingRelativeErrorResultName = "Average relative error (training)";
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| 40 | private const string TestRelativeErrorResultName = "Average relative error (test)";
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| 41 | private const string TrainingNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (training)";
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| 42 | private const string TestNormalizedMeanSquaredErrorResultName = "Normalized mean squared error (test)";
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| 43 | private const string TrainingDirectionalSymmetryResultName = "Average directional symmetry (training)";
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| 44 | private const string TestDirectionalSymmetryResultName = "Average directional symmetry (test)";
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| 45 | private const string TrainingWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (training)";
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| 46 | private const string TestWeightedDirectionalSymmetryResultName = "Average weighted directional symmetry (test)";
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| 47 | private const string TrainingTheilsUStatisticLastResultName = "Average Theil's U (last) (training)";
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| 48 | private const string TestTheilsUStatisticLastResultName = "Average Theil's U (last) (test)";
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| 49 | private const string TrainingTheilsUStatisticMeanResultName = "Average Theil's U (mean) (training)";
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| 50 | private const string TestTheilsUStatisticMeanResultName = "Average Theil's U (mean) (test)";
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| 51 | private const string TrainingTheilsUStatisticMaResultName = "Average Theil's U (moving average) (training)";
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| 52 | private const string TestTheilsUStatisticMaResultName = "Average Theil's U (moving average) (test)";
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| 53 |
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| 54 | public new ITimeSeriesPrognosisModel Model {
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| 55 | get { return (ITimeSeriesPrognosisModel)base.Model; }
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| 56 | protected set { base.Model = value; }
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| 57 | }
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| 58 |
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| 59 | public new ITimeSeriesPrognosisProblemData ProblemData {
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| 60 | get { return (ITimeSeriesPrognosisProblemData)base.ProblemData; }
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| 61 | set { base.ProblemData = value; }
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| 62 | }
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| 63 |
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| 64 | [Storable]
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| 65 | private int horizon;
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| 66 | public int Horizon {
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| 67 | get { return horizon; }
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| 68 | set {
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| 69 | if (horizon != value) {
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| 70 | horizon = value;
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| 71 | RecalculateResults();
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| 72 | }
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| 73 | }
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| 74 | }
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| 75 |
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| 76 | public abstract IEnumerable<IEnumerable<double>> GetPrognosedValues(IEnumerable<int> rows, IEnumerable<int> horizon);
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| 77 |
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| 78 | #region Results
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| 79 | public double TrainingMeanSquaredError {
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| 80 | get { return ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value; }
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| 81 | private set { ((DoubleValue)this[TrainingMeanSquaredErrorResultName].Value).Value = value; }
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| 82 | }
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| 83 | public double TestMeanSquaredError {
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| 84 | get { return ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value; }
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| 85 | private set { ((DoubleValue)this[TestMeanSquaredErrorResultName].Value).Value = value; }
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| 86 | }
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| 87 | public double TrainingMeanAbsoluteError {
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| 88 | get { return ((DoubleValue)this[TrainingMeanAbsoluteErrorResultName].Value).Value; }
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| 89 | private set { ((DoubleValue)this[TrainingMeanAbsoluteErrorResultName].Value).Value = value; }
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| 90 | }
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| 91 | public double TestMeanAbsoluteError {
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| 92 | get { return ((DoubleValue)this[TestMeanAbsoluteErrorResultName].Value).Value; }
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| 93 | private set { ((DoubleValue)this[TestMeanAbsoluteErrorResultName].Value).Value = value; }
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| 94 | }
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| 95 | public double TrainingRSquared {
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| 96 | get { return ((DoubleValue)this[TrainingSquaredCorrelationResultName].Value).Value; }
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| 97 | private set { ((DoubleValue)this[TrainingSquaredCorrelationResultName].Value).Value = value; }
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| 98 | }
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| 99 | public double TestRSquared {
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| 100 | get { return ((DoubleValue)this[TestSquaredCorrelationResultName].Value).Value; }
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| 101 | private set { ((DoubleValue)this[TestSquaredCorrelationResultName].Value).Value = value; }
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| 102 | }
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| 103 | public double TrainingRelativeError {
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| 104 | get { return ((DoubleValue)this[TrainingRelativeErrorResultName].Value).Value; }
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| 105 | private set { ((DoubleValue)this[TrainingRelativeErrorResultName].Value).Value = value; }
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| 106 | }
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| 107 | public double TestRelativeError {
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| 108 | get { return ((DoubleValue)this[TestRelativeErrorResultName].Value).Value; }
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| 109 | private set { ((DoubleValue)this[TestRelativeErrorResultName].Value).Value = value; }
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| 110 | }
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| 111 | public double TrainingNormalizedMeanSquaredError {
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| 112 | get { return ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value; }
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| 113 | private set { ((DoubleValue)this[TrainingNormalizedMeanSquaredErrorResultName].Value).Value = value; }
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| 114 | }
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| 115 | public double TestNormalizedMeanSquaredError {
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| 116 | get { return ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value; }
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| 117 | private set { ((DoubleValue)this[TestNormalizedMeanSquaredErrorResultName].Value).Value = value; }
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| 118 | }
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| 119 | public double TrainingDirectionalSymmetry {
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| 120 | get { return ((DoubleValue)this[TrainingDirectionalSymmetryResultName].Value).Value; }
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| 121 | private set { ((DoubleValue)this[TrainingDirectionalSymmetryResultName].Value).Value = value; }
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| 122 | }
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| 123 | public double TestDirectionalSymmetry {
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| 124 | get { return ((DoubleValue)this[TestDirectionalSymmetryResultName].Value).Value; }
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| 125 | private set { ((DoubleValue)this[TestDirectionalSymmetryResultName].Value).Value = value; }
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| 126 | }
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| 127 | public double TrainingWeightedDirectionalSymmetry {
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| 128 | get { return ((DoubleValue)this[TrainingWeightedDirectionalSymmetryResultName].Value).Value; }
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| 129 | private set { ((DoubleValue)this[TrainingWeightedDirectionalSymmetryResultName].Value).Value = value; }
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| 130 | }
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| 131 | public double TestWeightedDirectionalSymmetry {
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| 132 | get { return ((DoubleValue)this[TestWeightedDirectionalSymmetryResultName].Value).Value; }
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| 133 | private set { ((DoubleValue)this[TestWeightedDirectionalSymmetryResultName].Value).Value = value; }
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| 134 | }
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| 135 | public double TrainingTheilsUStatisticLast {
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| 136 | get { return ((DoubleValue)this[TrainingTheilsUStatisticLastResultName].Value).Value; }
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| 137 | private set { ((DoubleValue)this[TrainingTheilsUStatisticLastResultName].Value).Value = value; }
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| 138 | }
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| 139 | public double TestTheilsUStatisticLast {
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| 140 | get { return ((DoubleValue)this[TestTheilsUStatisticLastResultName].Value).Value; }
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| 141 | private set { ((DoubleValue)this[TestTheilsUStatisticLastResultName].Value).Value = value; }
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| 142 | }
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| 143 | public double TrainingTheilsUStatisticMean {
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| 144 | get { return ((DoubleValue)this[TrainingTheilsUStatisticMeanResultName].Value).Value; }
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| 145 | private set { ((DoubleValue)this[TrainingTheilsUStatisticMeanResultName].Value).Value = value; }
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| 146 | }
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| 147 | public double TestTheilsUStatisticMean {
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| 148 | get { return ((DoubleValue)this[TestTheilsUStatisticMeanResultName].Value).Value; }
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| 149 | private set { ((DoubleValue)this[TestTheilsUStatisticMeanResultName].Value).Value = value; }
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| 150 | }
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| 151 | public double TrainingTheilsUStatisticMovingAverage {
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| 152 | get { return ((DoubleValue)this[TrainingTheilsUStatisticMaResultName].Value).Value; }
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| 153 | private set { ((DoubleValue)this[TrainingTheilsUStatisticMaResultName].Value).Value = value; }
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| 154 | }
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| 155 | public double TestTheilsUStatisticMovingAverage {
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| 156 | get { return ((DoubleValue)this[TestTheilsUStatisticMaResultName].Value).Value; }
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| 157 | private set { ((DoubleValue)this[TestTheilsUStatisticMaResultName].Value).Value = value; }
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| 158 | }
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| 159 | #endregion
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| 160 |
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| 161 | [StorableConstructor]
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| 162 | protected TimeSeriesPrognosisSolutionBase(bool deserializing) : base(deserializing) { }
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| 163 | protected TimeSeriesPrognosisSolutionBase(TimeSeriesPrognosisSolutionBase original, Cloner cloner)
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| 164 | : base(original, cloner) {
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| 165 | this.horizon = original.horizon;
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| 166 | }
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| 167 | protected TimeSeriesPrognosisSolutionBase(ITimeSeriesPrognosisModel model, ITimeSeriesPrognosisProblemData problemData)
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| 168 | : base(model, problemData) {
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| 169 | Add(new Result(TrainingMeanSquaredErrorResultName, "Mean of squared errors of the model on the training partition", new DoubleValue()));
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| 170 | Add(new Result(TestMeanSquaredErrorResultName, "Mean of squared errors of the model on the test partition", new DoubleValue()));
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| 171 | Add(new Result(TrainingMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the training partition", new DoubleValue()));
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| 172 | Add(new Result(TestMeanAbsoluteErrorResultName, "Mean of absolute errors of the model on the test partition", new DoubleValue()));
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| 173 | Add(new Result(TrainingSquaredCorrelationResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the training partition", new DoubleValue()));
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| 174 | Add(new Result(TestSquaredCorrelationResultName, "Squared Pearson's correlation coefficient of the model output and the actual values on the test partition", new DoubleValue()));
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| 175 | Add(new Result(TrainingRelativeErrorResultName, "Average of the relative errors of the model output and the actual values on the training partition", new DoubleValue()));
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| 176 | Add(new Result(TestRelativeErrorResultName, "Average of the relative errors of the model output and the actual values on the test partition", new DoubleValue()));
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| 177 | Add(new Result(TrainingNormalizedMeanSquaredErrorResultName, "Normalized mean of squared errors of the model on the training partition", new DoubleValue()));
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| 178 | Add(new Result(TestNormalizedMeanSquaredErrorResultName, "Normalized mean of squared errors of the model on the test partition", new DoubleValue()));
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| 179 | Add(new Result(TrainingDirectionalSymmetryResultName, "The average directional symmetry of the forecasts of the model on the training partition", new DoubleValue()));
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| 180 | Add(new Result(TestDirectionalSymmetryResultName, "The average directional symmetry of the forecasts of the model on the test partition", new DoubleValue()));
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| 181 | Add(new Result(TrainingWeightedDirectionalSymmetryResultName, "The average weighted directional symmetry of the forecasts of the model on the training partition", new DoubleValue()));
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| 182 | Add(new Result(TestWeightedDirectionalSymmetryResultName, "The average weighted directional symmetry of the forecasts of the model on the test partition", new DoubleValue()));
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| 183 | Add(new Result(TrainingTheilsUStatisticLastResultName, "The average Theil's U statistic (reference: previous value) of the forecasts of the model on the training partition", new DoubleValue()));
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| 184 | Add(new Result(TestTheilsUStatisticLastResultName, "The average Theil's U statistic (reference: previous value) of the forecasts of the model on the test partition", new DoubleValue()));
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| 185 | Add(new Result(TrainingTheilsUStatisticMeanResultName, "The average Theil's U statistic (reference: mean value) of the forecasts of the model on the training partition", new DoubleValue()));
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| 186 | Add(new Result(TestTheilsUStatisticMeanResultName, "The average Theil's U statistic (reference: mean value) of the forecasts of the model on the test partition", new DoubleValue()));
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| 187 | Add(new Result(TrainingTheilsUStatisticMaResultName, "The average Theil's U statistic (reference: moving average) of the forecasts of the model on the training partition", new DoubleValue()));
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| 188 | Add(new Result(TestTheilsUStatisticMaResultName, "The average Theil's U statistic (reference: moving average) of the forecasts of the model on the test partition", new DoubleValue()));
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| 189 | horizon = 1;
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| 190 | }
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| 191 |
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| 192 |
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| 193 | protected void CalculateResults() {
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| 194 | OnlineCalculatorError errorState;
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| 195 | //mean model
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| 196 | double trainingMean = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes).Average();
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| 197 | var meanModel = new TimeSeriesPrognosisConstantModel(trainingMean);
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| 198 |
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| 199 | //AR1 model
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| 200 | double alpha, beta;
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| 201 | IEnumerable<double> trainingStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes.Select(r => r - 1).Where(r => r > 0)).ToList();
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| 202 | OnlineLinearScalingParameterCalculator.Calculate(ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndizes.Where(x => x > 0)), trainingStartValues, out alpha, out beta, out errorState);
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| 203 | var AR1model = new TimeSeriesPrognosisAutoRegressiveModel(alpha, beta, ProblemData.TargetVariable);
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| 204 |
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| 205 | //MA model
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| 206 | int movingAverageWindowSize = 10 + horizon;
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| 207 | var MovingAverageModel = new TimeSeriesPrognosisMovingAverageModel(movingAverageWindowSize, ProblemData.TargetVariable);
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| 208 |
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| 209 | #region Calculate training quality measures
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| 210 | var trainingHorizions = ProblemData.TrainingIndizes.Select(r => Math.Min(horizon, ProblemData.TrainingPartition.End - r)).ToList();
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| 211 | IEnumerable<IEnumerable<double>> trainingTargetValues = ProblemData.TrainingIndizes.Zip(trainingHorizions, Enumerable.Range).Select(r => ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, r)).ToList();
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| 212 | IEnumerable<IEnumerable<double>> trainingEstimatedValues = Model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndizes, trainingHorizions).ToList();
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| 213 |
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| 214 | TrainingMeanSquaredError = OnlineMeanSquaredErrorCalculator.Calculate(trainingTargetValues.SelectMany(x => x), trainingEstimatedValues.SelectMany(x => x), out errorState);
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| 215 | TrainingMeanSquaredError = errorState == OnlineCalculatorError.None ? TrainingMeanSquaredError : double.PositiveInfinity;
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| 216 | TrainingMeanAbsoluteError = OnlineMeanAbsoluteErrorCalculator.Calculate(trainingTargetValues.SelectMany(x => x), trainingEstimatedValues.SelectMany(x => x), out errorState);
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| 217 | TrainingMeanAbsoluteError = errorState == OnlineCalculatorError.None ? TrainingMeanAbsoluteError : double.PositiveInfinity;
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| 218 | TrainingRelativeError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(trainingTargetValues.SelectMany(x => x), trainingEstimatedValues.SelectMany(x => x), out errorState);
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| 219 | TrainingRelativeError = errorState == OnlineCalculatorError.None ? TrainingRelativeError : double.PositiveInfinity;
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| 220 | TrainingRSquared = OnlinePearsonsRSquaredCalculator.Calculate(trainingTargetValues.SelectMany(x => x), trainingEstimatedValues.SelectMany(x => x), out errorState);
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| 221 | TrainingRSquared = errorState == OnlineCalculatorError.None ? TrainingRSquared : 0.0;
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| 222 | TrainingNormalizedMeanSquaredError = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(trainingTargetValues.SelectMany(x => x), trainingEstimatedValues.SelectMany(x => x), out errorState);
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| 223 | TrainingNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? TrainingNormalizedMeanSquaredError : double.PositiveInfinity;
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| 224 |
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| 225 | IEnumerable<IEnumerable<double>> trainingMeanModelPredictions = meanModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndizes, trainingHorizions);
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| 226 | IEnumerable<IEnumerable<double>> trainingAR1ModelPredictions = AR1model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndizes, trainingHorizions);
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| 227 | IEnumerable<IEnumerable<double>> trainingMovingAverageModelPredictions = MovingAverageModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TrainingIndizes, trainingHorizions);
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| 228 |
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| 229 | TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
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| 230 | TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0;
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| 231 | TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingEstimatedValues, out errorState);
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| 232 | TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0;
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| 233 | TrainingTheilsUStatisticLast = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
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| 234 | TrainingTheilsUStatisticLast = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticLast : double.PositiveInfinity; ;
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| 235 | TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
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| 236 | TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity; ;
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| 237 | TrainingTheilsUStatisticMovingAverage = OnlineTheilsUStatisticCalculator.Calculate(trainingStartValues, trainingTargetValues, trainingMovingAverageModelPredictions, trainingEstimatedValues, out errorState);
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| 238 | TrainingTheilsUStatisticMovingAverage = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMovingAverage : double.PositiveInfinity; ; ;
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| 239 | #endregion
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| 240 |
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| 241 | #region Calculate test quality measures
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| 242 | var testHorizions = ProblemData.TestIndizes.Select(r => Math.Min(horizon, ProblemData.TestPartition.End - r)).ToList();
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| 243 | IEnumerable<IEnumerable<double>> testTargetValues = ProblemData.TestIndizes.Zip(testHorizions, Enumerable.Range).Select(r => ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, r)).ToList();
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| 244 | IEnumerable<IEnumerable<double>> testEstimatedValues = Model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndizes, testHorizions).ToList();
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| 245 |
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| 246 | TestMeanSquaredError = OnlineMeanSquaredErrorCalculator.Calculate(testTargetValues.SelectMany(x => x), testEstimatedValues.SelectMany(x => x), out errorState);
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| 247 | TestMeanSquaredError = errorState == OnlineCalculatorError.None ? TestMeanSquaredError : double.PositiveInfinity;
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| 248 | TestMeanAbsoluteError = OnlineMeanAbsoluteErrorCalculator.Calculate(testTargetValues.SelectMany(x => x), testEstimatedValues.SelectMany(x => x), out errorState);
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| 249 | TestMeanAbsoluteError = errorState == OnlineCalculatorError.None ? TestMeanAbsoluteError : double.PositiveInfinity;
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| 250 | TestRelativeError = OnlineMeanAbsolutePercentageErrorCalculator.Calculate(testTargetValues.SelectMany(x => x), testEstimatedValues.SelectMany(x => x), out errorState);
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| 251 | TestRelativeError = errorState == OnlineCalculatorError.None ? TestRelativeError : double.PositiveInfinity;
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| 252 | TestRSquared = OnlinePearsonsRSquaredCalculator.Calculate(testTargetValues.SelectMany(x => x), testEstimatedValues.SelectMany(x => x), out errorState);
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| 253 | TestRSquared = errorState == OnlineCalculatorError.None ? TestRSquared : 0.0;
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| 254 | TestNormalizedMeanSquaredError = OnlineNormalizedMeanSquaredErrorCalculator.Calculate(testTargetValues.SelectMany(x => x), testEstimatedValues.SelectMany(x => x), out errorState);
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| 255 | TestNormalizedMeanSquaredError = errorState == OnlineCalculatorError.None ? TestNormalizedMeanSquaredError : double.PositiveInfinity;
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| 256 |
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| 257 | IEnumerable<double> testStartValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndizes.Select(r => r - 1).Where(r => r > 0)).ToList();
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| 258 | IEnumerable<IEnumerable<double>> testMeanModelPredictions = meanModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndizes, testHorizions);
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| 259 | IEnumerable<IEnumerable<double>> testAR1ModelPredictions = AR1model.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndizes, testHorizions);
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| 260 | IEnumerable<IEnumerable<double>> testMovingAverageModelPredictions = MovingAverageModel.GetPrognosedValues(ProblemData.Dataset, ProblemData.TestIndizes, testHorizions);
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| 261 |
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| 262 | TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
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| 263 | TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0;
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| 264 | TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testStartValues, testTargetValues, testEstimatedValues, out errorState);
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| 265 | TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0;
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| 266 | TestTheilsUStatisticLast = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
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| 267 | TestTheilsUStatisticLast = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticLast : double.PositiveInfinity; ;
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| 268 | TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
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| 269 | TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity; ;
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| 270 | TestTheilsUStatisticMovingAverage = OnlineTheilsUStatisticCalculator.Calculate(testStartValues, testTargetValues, testMovingAverageModelPredictions, testEstimatedValues, out errorState);
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| 271 | TestTheilsUStatisticMovingAverage = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMovingAverage : double.PositiveInfinity; ; ;
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| 272 | #endregion
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| 273 | }
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| 274 | }
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| 275 | }
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