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source: trunk/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/Models/TimeSeriesPrognosisAutoRegressiveModel.cs @ 13921

Last change on this file since 13921 was 13921, checked in by bburlacu, 8 years ago

#2604: Revert changes to DataAnalysisSolution and IDataAnalysisSolution and implement the desired properties in model classes that implement IDataAnalysisModel, IRegressionModel and IClassificationModel.

File size: 4.8 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2015 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
28
29namespace HeuristicLab.Problems.DataAnalysis {
30  [StorableClass]
31  [Item("Autoregressive TimeSeries Model", "A linear autoregressive time series model used to predict future values.")]
32  public class TimeSeriesPrognosisAutoRegressiveModel : NamedItem, ITimeSeriesPrognosisModel {
33    public IEnumerable<string> VariablesUsedForPrediction {
34      get { return Enumerable.Empty<string>(); } // what to return here?
35    }
36
37    [Storable]
38    public double[] Phi { get; private set; }
39    [Storable]
40    public double Constant { get; private set; }
41    [Storable]
42    public string TargetVariable { get; private set; }
43
44    public int TimeOffset { get { return Phi.Length; } }
45
46    [StorableConstructor]
47    protected TimeSeriesPrognosisAutoRegressiveModel(bool deserializing) : base(deserializing) { }
48    protected TimeSeriesPrognosisAutoRegressiveModel(TimeSeriesPrognosisAutoRegressiveModel original, Cloner cloner)
49      : base(original, cloner) {
50      this.Phi = (double[])original.Phi.Clone();
51      this.Constant = original.Constant;
52      this.TargetVariable = original.TargetVariable;
53    }
54    public override IDeepCloneable Clone(Cloner cloner) {
55      return new TimeSeriesPrognosisAutoRegressiveModel(this, cloner);
56    }
57    public TimeSeriesPrognosisAutoRegressiveModel(string targetVariable, double[] phi, double constant)
58      : base("AR(1) Model") {
59      Phi = (double[])phi.Clone();
60      Constant = constant;
61      TargetVariable = targetVariable;
62    }
63
64    public IEnumerable<IEnumerable<double>> GetPrognosedValues(IDataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
65      var rowsEnumerator = rows.GetEnumerator();
66      var horizonsEnumerator = horizons.GetEnumerator();
67      var targetValues = dataset.GetReadOnlyDoubleValues(TargetVariable);
68      // produce a n-step forecast for all rows
69      while (rowsEnumerator.MoveNext() & horizonsEnumerator.MoveNext()) {
70        int row = rowsEnumerator.Current;
71        int horizon = horizonsEnumerator.Current;
72        if (row - TimeOffset < 0) {
73          yield return Enumerable.Repeat(double.NaN, horizon);
74          continue;
75        }
76
77        double[] prognosis = new double[horizon];
78        for (int h = 0; h < horizon; h++) {
79          double estimatedValue = 0.0;
80          for (int i = 1; i <= TimeOffset; i++) {
81            int offset = h - i;
82            if (offset >= 0) estimatedValue += prognosis[offset] * Phi[i - 1];
83            else estimatedValue += targetValues[row + offset] * Phi[i - 1];
84
85          }
86          estimatedValue += Constant;
87          prognosis[h] = estimatedValue;
88        }
89
90        yield return prognosis;
91      }
92
93      if (rowsEnumerator.MoveNext() || horizonsEnumerator.MoveNext())
94        throw new ArgumentException("Number of elements in rows and horizon enumerations doesn't match.");
95    }
96
97    public IEnumerable<double> GetEstimatedValues(IDataset dataset, IEnumerable<int> rows) {
98      var targetVariables = dataset.GetReadOnlyDoubleValues(TargetVariable);
99      foreach (int row in rows) {
100        double estimatedValue = 0.0;
101        if (row - TimeOffset < 0) {
102          yield return double.NaN;
103          continue;
104        }
105
106        for (int i = 1; i <= TimeOffset; i++) {
107          estimatedValue += targetVariables[row - i] * Phi[i - 1];
108        }
109        estimatedValue += Constant;
110        yield return estimatedValue;
111      }
112    }
113
114    public ITimeSeriesPrognosisSolution CreateTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData) {
115      return new TimeSeriesPrognosisSolution(this, new TimeSeriesPrognosisProblemData(problemData));
116    }
117    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {
118      throw new NotSupportedException();
119    }
120
121  }
122}
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