org.hawkular.datamining.forecast.models

Class ContinuousModel

java.lang.Object

org.hawkular.datamining.forecast.models.ContinuousModel

All Implemented Interfaces:

TimeSeriesModel

public class ContinuousModel
extends Object
implements TimeSeriesModel

Some models throw exception if model has not been initialized TimeSeriesModel.init(List) before calling TimeSeriesModel.learn(DataPoint). For example triple exponential smoothing needs initial data set at two times bigger than number of periods of the modelled time series.

This class collects learned points until initialization of the model is possible.

Author:

Pavol Loffay

Nested Class Summary

Nested classes/interfaces inherited from interface org.hawkular.datamining.forecast.models.TimeSeriesModel

TimeSeriesModel.TimestampComparator

Constructor Summary

Constructors

Constructor and Description
ContinuousModel(TimeSeriesModel model)

Method Summary

Modifier and Type	Method and Description
DataPoint	forecast() One step ahead prediction
List<DataPoint>	forecast(int nAhead) Multi step ahead prediction
AccuracyStatistics	init(List<DataPoint> learnData) Initialize model and return statistics - error of one step ahead prediction
AccuracyStatistics	initStatistics()
long	lastTimestamp()
void	learn(DataPoint learnData) Learn one point
void	learn(List<DataPoint> learnData) Learn multiple points
int	minimumInitSize()
String	name()
int	numberOfParams()
AccuracyStatistics	runStatistics()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

ContinuousModel

public ContinuousModel(TimeSeriesModel model)

Method Detail

init

public AccuracyStatistics init(List<DataPoint> learnData)

Description copied from interface: TimeSeriesModel

Initialize model and return statistics - error of one step ahead prediction

Specified by:

init in interface TimeSeriesModel

Parameters:

learnData - points to learn

Returns:

statistics for learnData

learn

public void learn(DataPoint learnData)

Description copied from interface: TimeSeriesModel

Learn one point

Specified by:

learn in interface TimeSeriesModel

Parameters:

learnData - point to learn

learn

public void learn(List<DataPoint> learnData)

Description copied from interface: TimeSeriesModel

Learn multiple points

Specified by:

learn in interface TimeSeriesModel

Parameters:

learnData - points to learn

forecast

public DataPoint forecast()

Description copied from interface: TimeSeriesModel

One step ahead prediction

Specified by:

forecast in interface TimeSeriesModel

Returns:

predicted point

forecast

public List<DataPoint> forecast(int nAhead)

Description copied from interface: TimeSeriesModel

Multi step ahead prediction

Specified by:

forecast in interface TimeSeriesModel

Parameters:

nAhead - number of steps for forecasting

Returns:

predicted points

initStatistics

public AccuracyStatistics initStatistics()

Specified by:

initStatistics in interface TimeSeriesModel

Returns:

initial statistics calculated when calling TimeSeriesModel.initStatistics()

runStatistics

public AccuracyStatistics runStatistics()

Specified by:

runStatistics in interface TimeSeriesModel

Returns:

statistics calculated on learning data

name

public String name()

Specified by:

name in interface TimeSeriesModel

Returns:

name of the implemented model

numberOfParams

public int numberOfParams()

Specified by:

numberOfParams in interface TimeSeriesModel

Returns:

number of parameters of the model, seasonal models include seasonal indices

minimumInitSize

public int minimumInitSize()

Specified by:

minimumInitSize in interface TimeSeriesModel

Returns:

number of parameters of the model, seasonal models include seasonal indices

lastTimestamp

public long lastTimestamp()

Specified by:

lastTimestamp in interface TimeSeriesModel