[8010] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[17181] | 3 | * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8010] | 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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[8486] | 24 | using System.Linq;
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[8010] | 25 | using HeuristicLab.Common;
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| 26 | using HeuristicLab.Core;
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[17097] | 27 | using HEAL.Attic;
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[8010] | 28 |
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| 29 | namespace HeuristicLab.Problems.DataAnalysis {
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[17097] | 30 | [StorableType("9C44E097-50F8-4EC1-BE1B-6A0246EC020E")]
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[8010] | 31 | [Item("Autoregressive TimeSeries Model", "A linear autoregressive time series model used to predict future values.")]
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[14027] | 32 | public class TimeSeriesPrognosisAutoRegressiveModel : RegressionModel, ITimeSeriesPrognosisModel {
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| 33 | public override IEnumerable<string> VariablesUsedForPrediction {
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| 34 | get { return new[] { TargetVariable }; }
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| 35 | }
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| 36 |
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[8010] | 37 | [Storable]
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[8430] | 38 | public double[] Phi { get; private set; }
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[8010] | 39 | [Storable]
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[8430] | 40 | public double Constant { get; private set; }
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[8010] | 41 |
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[8468] | 42 | public int TimeOffset { get { return Phi.Length; } }
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| 43 |
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[8010] | 44 | [StorableConstructor]
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[17097] | 45 | protected TimeSeriesPrognosisAutoRegressiveModel(StorableConstructorFlag _) : base(_) { }
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[8010] | 46 | protected TimeSeriesPrognosisAutoRegressiveModel(TimeSeriesPrognosisAutoRegressiveModel original, Cloner cloner)
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| 47 | : base(original, cloner) {
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[8430] | 48 | this.Phi = (double[])original.Phi.Clone();
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| 49 | this.Constant = original.Constant;
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[8010] | 50 | }
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| 51 | public override IDeepCloneable Clone(Cloner cloner) {
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| 52 | return new TimeSeriesPrognosisAutoRegressiveModel(this, cloner);
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| 53 | }
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[8468] | 54 | public TimeSeriesPrognosisAutoRegressiveModel(string targetVariable, double[] phi, double constant)
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[14027] | 55 | : base(targetVariable, "AR(1) Model") {
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[8468] | 56 | Phi = (double[])phi.Clone();
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| 57 | Constant = constant;
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[8010] | 58 | }
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| 59 |
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[12702] | 60 | public IEnumerable<IEnumerable<double>> GetPrognosedValues(IDataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
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[8010] | 61 | var rowsEnumerator = rows.GetEnumerator();
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| 62 | var horizonsEnumerator = horizons.GetEnumerator();
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[8468] | 63 | var targetValues = dataset.GetReadOnlyDoubleValues(TargetVariable);
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[8010] | 64 | // produce a n-step forecast for all rows
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| 65 | while (rowsEnumerator.MoveNext() & horizonsEnumerator.MoveNext()) {
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| 66 | int row = rowsEnumerator.Current;
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| 67 | int horizon = horizonsEnumerator.Current;
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[8486] | 68 | if (row - TimeOffset < 0) {
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| 69 | yield return Enumerable.Repeat(double.NaN, horizon);
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| 70 | continue;
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| 71 | }
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| 72 |
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[8010] | 73 | double[] prognosis = new double[horizon];
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[8468] | 74 | for (int h = 0; h < horizon; h++) {
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| 75 | double estimatedValue = 0.0;
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[8486] | 76 | for (int i = 1; i <= TimeOffset; i++) {
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[8468] | 77 | int offset = h - i;
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| 78 | if (offset >= 0) estimatedValue += prognosis[offset] * Phi[i - 1];
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| 79 | else estimatedValue += targetValues[row + offset] * Phi[i - 1];
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| 80 |
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| 81 | }
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| 82 | estimatedValue += Constant;
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| 83 | prognosis[h] = estimatedValue;
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| 84 | }
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| 85 |
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[8010] | 86 | yield return prognosis;
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| 87 | }
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| 88 |
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| 89 | if (rowsEnumerator.MoveNext() || horizonsEnumerator.MoveNext())
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| 90 | throw new ArgumentException("Number of elements in rows and horizon enumerations doesn't match.");
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| 91 | }
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| 92 |
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[14027] | 93 | public override IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
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[8468] | 94 | var targetVariables = dataset.GetReadOnlyDoubleValues(TargetVariable);
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| 95 | foreach (int row in rows) {
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| 96 | double estimatedValue = 0.0;
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[8486] | 97 | if (row - TimeOffset < 0) {
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| 98 | yield return double.NaN;
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| 99 | continue;
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| 100 | }
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| 101 |
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[8468] | 102 | for (int i = 1; i <= TimeOffset; i++) {
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| 103 | estimatedValue += targetVariables[row - i] * Phi[i - 1];
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| 104 | }
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| 105 | estimatedValue += Constant;
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| 106 | yield return estimatedValue;
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| 107 | }
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[8458] | 108 | }
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| 109 |
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[8010] | 110 | public ITimeSeriesPrognosisSolution CreateTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData) {
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[8857] | 111 | return new TimeSeriesPrognosisSolution(this, new TimeSeriesPrognosisProblemData(problemData));
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[8010] | 112 | }
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[14027] | 113 | public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
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[8458] | 114 | throw new NotSupportedException();
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| 115 | }
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[8010] | 116 |
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| 117 | }
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| 118 | }
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