#region License Information /* HeuristicLab * Copyright (C) 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 . */ #endregion using System; using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HEAL.Attic; namespace HeuristicLab.Problems.DataAnalysis { [StorableType("9C44E097-50F8-4EC1-BE1B-6A0246EC020E")] [Item("Autoregressive TimeSeries Model", "A linear autoregressive time series model used to predict future values.")] public class TimeSeriesPrognosisAutoRegressiveModel : RegressionModel, ITimeSeriesPrognosisModel { public override IEnumerable VariablesUsedForPrediction { get { return new[] { TargetVariable }; } } [Storable] public double[] Phi { get; private set; } [Storable] public double Constant { get; private set; } public int TimeOffset { get { return Phi.Length; } } [StorableConstructor] protected TimeSeriesPrognosisAutoRegressiveModel(StorableConstructorFlag _) : base(_) { } protected TimeSeriesPrognosisAutoRegressiveModel(TimeSeriesPrognosisAutoRegressiveModel original, Cloner cloner) : base(original, cloner) { this.Phi = (double[])original.Phi.Clone(); this.Constant = original.Constant; } public override IDeepCloneable Clone(Cloner cloner) { return new TimeSeriesPrognosisAutoRegressiveModel(this, cloner); } public TimeSeriesPrognosisAutoRegressiveModel(string targetVariable, double[] phi, double constant) : base(targetVariable, "AR(1) Model") { Phi = (double[])phi.Clone(); Constant = constant; } public IEnumerable> GetPrognosedValues(IDataset dataset, IEnumerable rows, IEnumerable horizons) { var rowsEnumerator = rows.GetEnumerator(); var horizonsEnumerator = horizons.GetEnumerator(); var targetValues = dataset.GetReadOnlyDoubleValues(TargetVariable); // produce a n-step forecast for all rows while (rowsEnumerator.MoveNext() & horizonsEnumerator.MoveNext()) { int row = rowsEnumerator.Current; int horizon = horizonsEnumerator.Current; if (row - TimeOffset < 0) { yield return Enumerable.Repeat(double.NaN, horizon); continue; } double[] prognosis = new double[horizon]; for (int h = 0; h < horizon; h++) { double estimatedValue = 0.0; for (int i = 1; i <= TimeOffset; i++) { int offset = h - i; if (offset >= 0) estimatedValue += prognosis[offset] * Phi[i - 1]; else estimatedValue += targetValues[row + offset] * Phi[i - 1]; } estimatedValue += Constant; prognosis[h] = estimatedValue; } yield return prognosis; } if (rowsEnumerator.MoveNext() || horizonsEnumerator.MoveNext()) throw new ArgumentException("Number of elements in rows and horizon enumerations doesn't match."); } public override IEnumerable GetEstimatedValues(IDataset dataset, IEnumerable rows) { var targetVariables = dataset.GetReadOnlyDoubleValues(TargetVariable); foreach (int row in rows) { double estimatedValue = 0.0; if (row - TimeOffset < 0) { yield return double.NaN; continue; } for (int i = 1; i <= TimeOffset; i++) { estimatedValue += targetVariables[row - i] * Phi[i - 1]; } estimatedValue += Constant; yield return estimatedValue; } } public ITimeSeriesPrognosisSolution CreateTimeSeriesPrognosisSolution(ITimeSeriesPrognosisProblemData problemData) { return new TimeSeriesPrognosisSolution(this, new TimeSeriesPrognosisProblemData(problemData)); } public override IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) { throw new NotSupportedException(); } } }