weka.classifiers.timeseries.AbstractForecaster Maven / Gradle / Ivy
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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.
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/*
* 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_fieldsToForecast = null;
/**
* A utility method for converting a List of Strings to a single
* comma separated String.
*
* @param list the List object to process
* @return a single String containing a comma separated list of
* elements from the original list.
*/
public static List stringToList(String list) {
String[] fieldNames = list.split(",");
List thelist = new ArrayList();
for (String f : fieldNames) {
thelist.add(f);
}
return thelist;
}
/**
* Set the names of the fields/attributes in the data to forecast.
*
* @param targets a list of names of fields to forecast
* @throws Exception if a field(s) can't be found, or if multiple
* fields are specified and this forecaster can't predict multiple
* fields.
*/
public void setFieldsToForecast(String targets) throws Exception {
m_fieldsToForecast = stringToList(targets);
}
/**
* Get the fields to forecast.
*
* @return the fields to forecast
*/
public String getFieldsToForecast() {
String list = "";
for (String f : m_fieldsToForecast) {
list += (f + ",");
}
list = list.substring(0, list.lastIndexOf(','));
return list;
}
/**
* Builds a new forecasting model using the supplied training
* data. The instances in the data are assumed to be sorted in
* ascending order of time and equally spaced in time. Some
* methods may not need to implement this method and may
* instead do their work in the primeForecaster method.
*
* @param insts the training instances.
* @param progress an optional varargs parameter supplying progress objects
* to report to
* @throws Exception if the model can't be constructed for some
* reason.
*/
public abstract void buildForecaster(Instances insts,
PrintStream... progress) throws Exception;
/**
* Supply the (potentially) trained model with enough historical
* data, up to and including the current time point, in order
* to produce a forecast. Instances are assumed to be sorted in
* ascending order of time and equally spaced in time.
*
* @param insts the instances to prime the model with
* @throws Exception if the model can't be primed for some
* reason.
*/
public abstract void primeForecaster(Instances insts) throws Exception;
/**
* Produce a forecast for the target field(s).
* Assumes that the model has been built
* and/or primed so that a forecast can be generated.
*
* @param numSteps number of forecasted values to produce for each target. E.g.
* a value of 5 would produce a prediction for t+1, t+2, ..., t+5.
* @param progress an optional varargs parameter supplying progress objects
* to report to
* @return a List of Lists (one for each step) of forecasted values for each target
* @throws Exception if the forecast can't be produced for some reason.
*/
public abstract List> forecast(int numSteps,
PrintStream... progress) throws Exception;
@Override
public void preExecution() throws Exception {
// subclasses to override
}
@Override
public void postExecution() throws Exception {
// subclasses to override
}
/**
* Run the supplied object using the supplied options on the command line.
*
* @param toRun the object to run.
* @param options the command line options to pass to the object.
* @throws Exception if the supplied object is not an instance of TSForecaster.
*/
public void run(Object toRun, String[] options) throws IllegalArgumentException {
if (!(toRun instanceof TSForecaster)) {
throw new IllegalArgumentException("Argument must be an object of type" +
" TSForecaster!");
}
runForecaster((TSForecaster)toRun, options);
}
/**
* Creates a deep copy of the given forecaster using serialization.
*
* @param model the forecaster to copy
* @return a deep copy of the forecaster
* @exception Exception if an error occurs
*/
public static TSForecaster makeCopy(TSForecaster model) throws Exception {
return (TSForecaster)new SerializedObject(model).getObject();
}
/**
* Run the supplied forecaster with the supplied options on the command line.
*
* @param forecaster the forecaster to run
* @param options the options to pass to the forecaster
*/
public void runForecaster(TSForecaster forecaster, String[] options) {
try {
TSEvaluation.evaluateForecaster(forecaster, options);
} catch (Exception e) {
if ( ((e.getMessage() != null) &&
(e.getMessage().indexOf("General options") == -1))
|| (e.getMessage() == null) ) {
e.printStackTrace();
} else {
System.err.println(e.getMessage());
}
}
}
}