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Provides a time series forecasting environment for Weka. Includes a wrapper for Weka regression schemes that automates the process of creating lagged variables and date-derived periodic variables and provides the ability to do closed-loop forecasting. New evaluation routines are provided by a special evaluation module and graphing of predictions/forecasts are provided via the JFreeChart library. Includes both command-line and GUI user interfaces. Sample time series data can be found in ${WEKA_HOME}/packages/timeseriesForecasting/sample-data.
/*
* This program 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.
*
* This program 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 this program. If not, see > m_predictions;
/** The counts of each valid target prediction */
protected double[] m_counts;
/**
* Reset this module
*/
public void reset() {
if (m_targetFieldNames != null) {
m_predictions = new ArrayList>();
m_counts = new double[m_targetFieldNames.size()];
for (int i = 0; i < m_targetFieldNames.size(); i++) {
ArrayList predsForTarget =
new ArrayList();
m_predictions.add(predsForTarget);
}
}
}
/**
* Return the short identifying name of this evaluation module
*
* @return the short identifying name of this evaluation module
*/
public String getEvalName() {
return "Error";
}
/**
* Return the longer (single sentence) description
* of this evaluation module
*
* @return the longer description of this module
*/
public String getDescription() {
return "Sum of errors";
}
/**
* Return the mathematical formula that this
* evaluation module computes.
*
* @return the mathematical formula that this module
* computes.
*/
public String getDefinition() {
return "sum(predicted - actual)";
}
/**
* Gets a textual description of this module : getDescription() + getEvalName()
*/
public String toString() {
return getDescription() + " (" + getEvalName() + ")";
}
/**
* Evaluate the given forecast(s) with respect to the given
* test instance. Targets with missing values are ignored.
*
* @param forecasts a List of forecasted values. Each element
* corresponds to one of the targets and is assumed to be in the same
* order as the list of targets supplied to the setTargetFields() method.
* @throws Exception if the evaluation can't be completed for some
* reason.
*/
public void evaluateForInstance(List forecasts, Instance inst)
throws Exception {
if (m_predictions == null) {
throw new Exception("Target fields haven't been set yet!");
}
if (forecasts.size() != m_targetFieldNames.size()) {
throw new Exception("The number of forecasted values does not match the" +
" number of target fields!");
}
for (int i = 0; i < m_targetFieldNames.size(); i++) {
double actualValue = getTargetValue(m_targetFieldNames.get(i), inst);
double predictedValue = forecasts.get(i).predicted();
//System.err.println("Actual: " + actualValue + " Predicted: " + predictedValue);
double[][] intervals = forecasts.get(i).predictionIntervals();
NumericPrediction pred = new NumericPrediction(actualValue, predictedValue, 1, intervals);
m_predictions.get(i).add(pred);
if (!Utils.isMissingValue(predictedValue) &&
!Utils.isMissingValue(actualValue)) {
m_counts[i]++;
}
}
}
/**
* Calculate the measure that this module represents.
*
* @return the value of the measure for this module for each
* of the target(s).
* @throws Exception if the measure can't be computed for some reason.
*/
public double[] calculateMeasure() throws Exception {
if (m_predictions == null || m_predictions.get(0).size() == 0) {
throw new Exception("No predictions have been seen yet!");
}
double[] result = new double[m_targetFieldNames.size()];
for (int i = 0; i < m_targetFieldNames.size(); i++) {
List preds = m_predictions.get(i);
double sumOfE = 0;
for (NumericPrediction p : preds) {
if (!Utils.isMissingValue(p.error())) {
sumOfE += p.error();
}
}
result[i] = sumOfE;
}
return result;
}
/**
* Gets the number of predicted, actual pairs for each target. Only
* entries that are non-missing for both actual and predicted contribute
* to the overall count.
*
* @return the number of predicted, actual pairs for each target.
* @throws Exception
*/
public double[] countsForTargets() throws Exception {
if (m_predictions == null || m_predictions.get(0).size() == 0) {
throw new Exception("No predictions have been seen yet!");
}
return m_counts;
}
/**
* Get a list of the errors for the supplied target
*
* @param targetName the target to get the errors for
* @return the errors as a list of Double
* @throws IllegalArgumentException if the target name is unknown
*/
public List getErrorsForTarget(String targetName)
throws IllegalArgumentException {
for (int i = 0; i < m_targetFieldNames.size(); i++) {
if (m_targetFieldNames.get(i).equals(targetName)) {
ArrayList errors = new ArrayList();
List preds = m_predictions.get(i);
for (int j = 0; j < preds.size(); j++) {
Double err = new Double(preds.get(j).error());
errors.add(err);
}
return errors;
}
}
throw new IllegalArgumentException("Unknown target: " + targetName);
}
/**
* Get a list of predictions (plus actuals if known) for the supplied target
*
* @param targetName the target to get predictions for
* @return a list of predictions for the supplied target
* @throws IllegalArgumentException if the target name is unknown
*/
public List getPredictionsForTarget(String targetName)
throws IllegalArgumentException {
for (int i = 0; i < m_targetFieldNames.size(); i++) {
if (m_targetFieldNames.get(i).equals(targetName)) {
return m_predictions.get(i);
}
}
throw new IllegalArgumentException("Unknown target: " + targetName);
}
/**
* Gets the predictions for all targets
*
* @return the predictions for all targets as a list of lists the outer list
* indexes targets.
*/
public List> getPredictionsForAllTargets() {
return m_predictions;
}
public String toSummaryString() throws Exception {
StringBuffer result = new StringBuffer();
double[] measures = calculateMeasure();
for (int i = 0; i < m_targetFieldNames.size(); i++) {
result.append(getDescription() + " (" + m_targetFieldNames.get(i) + "): "
+ Utils.doubleToString(measures[i], 4) + " (n = " + m_counts[i] + ")");
result.append("\n");
}
return result.toString();
}
}