org.datahack.forecast.BayExample.java Source code

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Here is the source code for org.datahack.forecast.BayExample.java

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package org.datahack.forecast;

import java.io.*;
import java.net.URL;
import java.util.List;
import org.apache.commons.io.FileUtils;
import weka.classifiers.evaluation.NumericPrediction;
import weka.classifiers.functions.GaussianProcesses;
import weka.classifiers.timeseries.WekaForecaster;
import weka.core.Instances;
import weka.experiment.InstanceQuery;

/**
 * Example of using the time series forecasting API. To compile and
 * run the CLASSPATH will need to contain:
 *
 * weka.jar (from your weka distribution)
 * pdm-timeseriesforecasting-ce-TRUNK-SNAPSHOT.jar (from the time series package)
 * jcommon-1.0.14.jar (from the time series package lib directory)
 * jfreechart-1.0.13.jar (from the time series package lib directory)
 */
public class BayExample {

    public static void main(String[] args) {
        try {
            // path to the Australian wine data included with the time series forecasting
            // package

            File output = File.createTempFile("tempWineData", "arff");
            output.deleteOnExit();
            String dataFileName = "sample-data/wine.arff";
            URL resource = BayExample.class.getResource("/" + dataFileName);
            FileUtils.copyURLToFile(resource, output);

            String pathToWineData = output.getPath();

            // load the wine data
            Instances wine = new Instances(new BufferedReader(new FileReader(pathToWineData)));

            InstanceQuery q = new InstanceQuery();

            // new forecaster
            WekaForecaster forecaster = new WekaForecaster();

            // set the targets we want to forecast. This method calls
            // setFieldsToLag() on the lag maker object for us
            forecaster.setFieldsToForecast("Fortified,Dry-white");

            // default underlying classifier is SMOreg (SVM) - we'll use
            // gaussian processes for regression instead
            forecaster.setBaseForecaster(new GaussianProcesses());

            forecaster.getTSLagMaker().setTimeStampField("Date"); // date time stamp
            forecaster.getTSLagMaker().setMinLag(1);
            forecaster.getTSLagMaker().setMaxLag(12); // monthly data

            // add a month of the year indicator field
            forecaster.getTSLagMaker().setAddMonthOfYear(true);

            // add a quarter of the year indicator field
            forecaster.getTSLagMaker().setAddQuarterOfYear(true);

            // build the model
            forecaster.buildForecaster(wine, System.out);

            // prime the forecaster with enough recent historical data
            // to cover up to the maximum lag. In our case, we could just supply
            // the 12 most recent historical instances, as this covers our maximum
            // lag period
            forecaster.primeForecaster(wine);

            // forecast for 12 units (months) beyond the end of the
            // training data
            List<List<NumericPrediction>> forecast = forecaster.forecast(12, System.out);

            // output the predictions. Outer list is over the steps; inner list is over
            // the targets
            for (int i = 0; i < 12; i++) {
                List<NumericPrediction> predsAtStep = forecast.get(i);
                for (int j = 0; j < 2; j++) {
                    NumericPrediction predForTarget = predsAtStep.get(j);
                    System.out.print("" + predForTarget.predicted() + " ");
                }
                System.out.println();
            }

            // we can continue to use the trained forecaster for further forecasting
            // by priming with the most recent historical data (as it becomes available).
            // At some stage it becomes prudent to re-build the model using current
            // historical data.

        } catch (Exception ex) {
            ex.printStackTrace();
        }
    }
}