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Time Series Analysis in Java
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/*
* Copyright (c) 2016 Jacob Rachiele
*
* Permission is hereby granted, free of charge, to any person obtaining a copy of this software
* and associated documentation files (the "Software"), to deal in the Software without restriction
* including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense
* and/or sell copies of the Software, and to permit persons to whom the Software is furnished to
* do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all copies or
* substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED
* INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR
* PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
* LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
* USE OR OTHER DEALINGS IN THE SOFTWARE.
*
* Contributors:
*
* Jacob Rachiele
*/
package timeseries.models;
import com.google.common.primitives.Doubles;
import org.knowm.xchart.XChartPanel;
import org.knowm.xchart.XYChart;
import org.knowm.xchart.XYChartBuilder;
import org.knowm.xchart.XYSeries;
import org.knowm.xchart.XYSeries.XYSeriesRenderStyle;
import org.knowm.xchart.style.Styler.ChartTheme;
import org.knowm.xchart.style.markers.Circle;
import org.knowm.xchart.style.markers.None;
import stats.distributions.StudentsT;
import timeseries.TimeSeries;
import javax.swing.*;
import java.awt.*;
import java.time.OffsetDateTime;
import java.util.ArrayList;
import java.util.Date;
import java.util.List;
/**
* A mean model forecast.
*
* @author Jacob Rachiele
*/
public final class MeanForecast implements Forecast {
private final Model model;
private final TimeSeries forecast;
private final TimeSeries upperValues;
private final TimeSeries lowerValues;
private final TimeSeries fcstErrors;
public MeanForecast(final Model model, final int steps, final double alpha) {
if (steps < 1) {
throw new IllegalArgumentException("The number of steps ahead to forecast must be greater" +
" than or equal to 1, but was " + steps);
}
if (alpha < 0 || alpha > 1) {
throw new IllegalArgumentException("The value of alpha must be between 0 and 1, but was " + alpha);
}
this.model = model;
this.forecast = model.pointForecast(steps);
this.fcstErrors = getFcstErrors(steps, alpha);
this.upperValues = computeUpperPredictionBounds();
this.lowerValues = computeLowerPredictionBounds();
}
public MeanForecast(final Model model) {
this(model, 12, 0.05);
}
@Override
public TimeSeries forecast() {
return this.forecast;
}
@Override
public TimeSeries upperPredictionValues() {
return this.upperValues;
}
@Override
public TimeSeries lowerPredictionValues() {
return this.lowerValues;
}
@Override
public TimeSeries computeUpperPredictionBounds(final int steps, final double alpha) {
if (steps < 1) {
throw new IllegalArgumentException("The number of steps ahead to forecast must be greater" +
" than or equal to 1, but was " + steps);
}
if (alpha < 0 || alpha > 1) {
throw new IllegalArgumentException("The value of alpha must be between 0 and 1, but was " + alpha);
}
TimeSeries forecast = model.pointForecast(steps);
TimeSeries fcstStdError = getFcstErrors(steps, alpha);
double[] upperPredictionValues = new double[steps];
for (int t = 0; t < steps; t++) {
upperPredictionValues[t] = forecast.at(t) + fcstStdError.at(t);
}
return new TimeSeries(forecast.timePeriod(), forecast.observationTimes().get(0), upperPredictionValues);
}
@Override
public TimeSeries computeLowerPredictionBounds(final int steps, final double alpha) {
if (steps < 1) {
throw new IllegalArgumentException("The number of steps ahead to forecast must be greater" +
" than or equal to 1, but was " + steps);
}
if (alpha < 0 || alpha > 1) {
throw new IllegalArgumentException("The value of alpha must be between 0 and 1, but was " + alpha);
}
TimeSeries forecast = model.pointForecast(steps);
double[] lowerPredictionValues = new double[steps];
TimeSeries fcstStdError = getFcstErrors(steps, alpha);
for (int t = 0; t < steps; t++) {
lowerPredictionValues[t] = forecast.at(t) - fcstStdError.at(t);
}
return new TimeSeries(forecast.timePeriod(), forecast.observationTimes().get(0), lowerPredictionValues);
}
@Override
public void plotForecast() {
new Thread(() -> {
final List xAxis = new ArrayList<>(forecast.observationTimes().size());
for (OffsetDateTime dateTime : forecast.observationTimes()) {
xAxis.add(Date.from(dateTime.toInstant()));
}
List errorList = Doubles.asList(fcstErrors.asArray());
List forecastList = Doubles.asList(forecast.asArray());
final XYChart chart = new XYChartBuilder().theme(ChartTheme.GGPlot2).height(600).width(800)
.title("Mean Forecast").build();
chart.setXAxisTitle("Time");
chart.setYAxisTitle("Forecast Values");
chart.getStyler().setAxisTitleFont(new Font("Arial", Font.PLAIN, 14)).setMarkerSize(5);
chart.getStyler().setDefaultSeriesRenderStyle(XYSeriesRenderStyle.Line).setErrorBarsColor(Color.RED)
.setChartFontColor(new Color(112, 112, 112));
XYSeries forecastSeries = chart.addSeries("Forecast", xAxis, forecastList, errorList);
forecastSeries.setMarker(new Circle()).setMarkerColor(Color.BLACK).setLineWidth(1.5f)
.setLineColor(Color.BLUE);
JPanel panel = new XChartPanel<>(chart);
JFrame frame = new JFrame("Mean Forecast");
frame.setDefaultCloseOperation(JFrame.DISPOSE_ON_CLOSE);
frame.add(panel);
frame.pack();
frame.setVisible(true);
}).start();
}
@Override
public void plot() {
new Thread(() -> {
final List xAxis = new ArrayList<>(forecast.observationTimes().size());
final List xAxisObs = new ArrayList<>(model.timeSeries().n());
for (OffsetDateTime dateTime : model.timeSeries().observationTimes()) {
xAxisObs.add(Date.from(dateTime.toInstant()));
}
for (OffsetDateTime dateTime : forecast.observationTimes()) {
xAxis.add(Date.from(dateTime.toInstant()));
}
List errorList = Doubles.asList(fcstErrors.asArray());
List seriesList = Doubles.asList(model.timeSeries().asArray());
List forecastList = Doubles.asList(forecast.asArray());
final XYChart chart = new XYChartBuilder().theme(ChartTheme.GGPlot2).height(800).width(1200)
.title("Mean Forecast Past and Future").build();
XYSeries observationSeries = chart.addSeries("Past", xAxisObs, seriesList);
XYSeries forecastSeries = chart.addSeries("Future", xAxis, forecastList, errorList);
observationSeries.setMarker(new None());
forecastSeries.setMarker(new None());
observationSeries.setLineWidth(0.75f);
forecastSeries.setLineWidth(1.5f);
chart.getStyler().setDefaultSeriesRenderStyle(XYSeriesRenderStyle.Line).setErrorBarsColor(Color.RED);
observationSeries.setLineColor(Color.BLACK);
forecastSeries.setLineColor(Color.BLUE);
JPanel panel = new XChartPanel<>(chart);
JFrame frame = new JFrame("Mean Forecast Past and Future");
frame.setDefaultCloseOperation(JFrame.DISPOSE_ON_CLOSE);
frame.add(panel);
frame.pack();
frame.setVisible(true);
}).start();
}
private TimeSeries computeUpperPredictionBounds() {
double[] upperPredictionValues = new double[this.forecast.n()];
for (int t = 0; t < this.forecast.n(); t++) {
upperPredictionValues[t] = forecast.at(t) + this.fcstErrors.at(t);
}
return new TimeSeries(forecast.timePeriod(), forecast.observationTimes().get(0), upperPredictionValues);
}
private TimeSeries computeLowerPredictionBounds() {
double[] lowerPredictionValues = new double[this.forecast.n()];
for (int t = 0; t < this.forecast.n(); t++) {
lowerPredictionValues[t] = forecast.at(t) - this.fcstErrors.at(t);
}
return new TimeSeries(forecast.timePeriod(), forecast.observationTimes().get(0), lowerPredictionValues);
}
private TimeSeries getFcstErrors(final int steps, final double alpha) {
double[] errors = new double[steps];
double criticalValue = new StudentsT(model.timeSeries().n() - 1).quantile(1 - alpha / 2);
double variance = model.timeSeries().variance();
double meanStdError = variance / model.timeSeries().n();
double fcstStdError = Math.sqrt(variance + meanStdError);
for (int t = 0; t < errors.length; t++) {
errors[t] = criticalValue * fcstStdError;
}
return new TimeSeries(forecast.timePeriod(), forecast.observationTimes().get(0), errors);
}
}