#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();
}
}
}