weka.classifiers.timeseries

Interface TSForecaster

All Known Implementing Classes:

AbstractForecaster, WekaForecaster

public interface TSForecaster

Interface for something that can produce time series predictions.

Version:

$Revision: 45163 $

Author:

Mark Hall (mhall{[at]}pentaho{[dot]}com)

Method Summary

Methods

Modifier and Type	Method and Description
boolean	baseModelHasSerializer() Check whether the base learner requires special serialization
void	buildForecaster(weka.core.Instances insts, PrintStream... progress) Builds a new forecasting model using the supplied training data.
void	clearPreviousState() Reset model state.
List<List<weka.classifiers.evaluation.NumericPrediction>>	forecast(int numSteps, PrintStream... progress) Produce a forecast for the target field(s).
String	getAlgorithmName() Provides a short name that describes the underlying algorithm in some way.
String	getFieldsToForecast() Get the fields to forecast.
List<Object>	getPreviousState() Get the last set state of the model.
void	loadBaseModel(String filepath) Load serialized classifier
void	loadSerializedState(String filepath) Load serialized model state
void	primeForecaster(weka.core.Instances insts) Supply the (potentially) trained model with enough historical data, up to and including the current time point, in order to produce a forecast.
void	reset() Reset this forecaster so that it is ready to construct a new model.
void	runForecaster(TSForecaster forecaster, String[] options) Run the supplied forecaster with the supplied options on the command line.
void	saveBaseModel(String filepath) Save underlying classifier
void	serializeState(String filepath) Serialize model state
void	setFieldsToForecast(String targets) Set the names of the fields/attributes in the data to forecast.
void	setPreviousState(List<Object> previousState) Load state into model.
boolean	usesState() Check whether the base learner requires operations regarding state

Method Detail

baseModelHasSerializer

boolean baseModelHasSerializer()

Check whether the base learner requires special serialization

Returns:

true if base learner requires special serialization, false otherwise

saveBaseModel

void saveBaseModel(String filepath)
                   throws Exception

Save underlying classifier

Parameters:

filepath - the path of the file to save the base model to

Throws:

Exception

loadBaseModel

void loadBaseModel(String filepath)
                   throws Exception

Load serialized classifier

Parameters:

filepath - the path of the file to load the base model from

Throws:

Exception

usesState

boolean usesState()

Check whether the base learner requires operations regarding state

Returns:

true if base learner uses state-based predictions, false otherwise

clearPreviousState

void clearPreviousState()

Reset model state.

setPreviousState

void setPreviousState(List<Object> previousState)

Load state into model.

getPreviousState

List<Object> getPreviousState()

Get the last set state of the model.

Returns:

the state of the model to be used in next prediction

serializeState

void serializeState(String filepath)
                    throws Exception

Serialize model state

Parameters:

filepath - the path of the file to save the model state to

Throws:

Exception

loadSerializedState

void loadSerializedState(String filepath)
                         throws Exception

Load serialized model state

Parameters:

filepath - the path of the file to save the model state from

Throws:

Exception

getAlgorithmName

String getAlgorithmName()

Provides a short name that describes the underlying algorithm in some way.

Returns:

a short description of this forecaster.

reset

void reset()

Reset this forecaster so that it is ready to construct a new model.

setFieldsToForecast

void setFieldsToForecast(String targets)
                         throws Exception

Set the names of the fields/attributes in the data to forecast.

Parameters:

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.

getFieldsToForecast

String getFieldsToForecast()

Get the fields to forecast.

Returns:

the fields to forecast

buildForecaster

void buildForecaster(weka.core.Instances insts,
                   PrintStream... progress)
                     throws Exception

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.

Parameters:

insts - the training instances.

progress - an optional varargs parameter supplying progress objects to report/log to

Throws:

Exception - if the model can't be constructed for some reason.

primeForecaster

void primeForecaster(weka.core.Instances insts)
                     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.

Parameters:

insts - the instances to prime the model with

Throws:

Exception - if the model can't be primed for some reason.

forecast

List<List<weka.classifiers.evaluation.NumericPrediction>> forecast(int numSteps,
                                                                 PrintStream... progress)
                                                                   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.

Parameters:

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.

progress - an optional varargs parameter supplying progress objects to report/log to

Returns:

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.

runForecaster

void runForecaster(TSForecaster forecaster,
                 String[] options)

Run the supplied forecaster with the supplied options on the command line.

Parameters:

forecaster - the forecaster to run

options - the options to pass to the forecaster