gov.sandia.cognition.learning.algorithm

Class TimeSeriesPredictionLearner<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.learning.algorithm.TimeSeriesPredictionLearner<InputType,OutputType,EvaluatorType>

Type Parameters:

InputType - Type of inputs to the prediction engine.

OutputType - Type of outputs to predict from the prediction engine.

EvaluatorType - Type of evaluator produced by the learning algorithm.

All Implemented Interfaces:

BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,EvaluatorType>, SupervisedBatchLearner<InputType,OutputType,EvaluatorType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

public class TimeSeriesPredictionLearner<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>
extends AbstractCloneableSerializable
implements SupervisedBatchLearner<InputType,OutputType,EvaluatorType>

A learner used to predict the future of a sequence of data by wrapping another learner and created a future-aligned data set.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_PREDICTION_HORIZON Default prediction horizon, 1.

Constructor Summary

Constructors

Constructor and Description
TimeSeriesPredictionLearner() Default constructor
TimeSeriesPredictionLearner(int predictionHorizon) Creates a new instance of TimeSeriesPredictionLearner
TimeSeriesPredictionLearner(int predictionHorizon, SupervisedBatchLearner<InputType,OutputType,EvaluatorType> supervisedLearner) Creates a new instance of TimeSeriesPredictionLearner

Method Summary

Modifier and Type	Method and Description
static <InputType,OutputType> java.util.ArrayList<InputOutputPair<InputType,OutputType>>	createPredictionDataset(int predictionHorizon, java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) Creates a dataset that can be used to predict the future by "predictionHorizon" samples.
int	getPredictionHorizon() Getter for predictionHorizon
SupervisedBatchLearner<InputType,OutputType,EvaluatorType>	getSupervisedLearner() Getter for supervisedLearner
EvaluatorType	learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
void	setPredictionHorizon(int predictionHorizon) Setter for predictionHorizon
void	setSupervisedLearner(SupervisedBatchLearner<InputType,OutputType,EvaluatorType> supervisedLearner) Setter for supervisedLearner

Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable

clone

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Field Detail

DEFAULT_PREDICTION_HORIZON

public static final int DEFAULT_PREDICTION_HORIZON

Default prediction horizon, 1.

See Also:

Constant Field Values

Constructor Detail

TimeSeriesPredictionLearner

public TimeSeriesPredictionLearner()

Default constructor

TimeSeriesPredictionLearner

public TimeSeriesPredictionLearner(int predictionHorizon)

Creates a new instance of TimeSeriesPredictionLearner

Parameters:

predictionHorizon - Number of samples into the future to predict.

TimeSeriesPredictionLearner

public TimeSeriesPredictionLearner(int predictionHorizon,
                                   SupervisedBatchLearner<InputType,OutputType,EvaluatorType> supervisedLearner)

Creates a new instance of TimeSeriesPredictionLearner

Parameters:

predictionHorizon - Number of samples into the future to predict.

supervisedLearner - Learning algorithm that does the heavy lifting.

Method Detail

getPredictionHorizon

public int getPredictionHorizon()

Getter for predictionHorizon

Returns:

Number of samples into the future to predict.

setPredictionHorizon

public void setPredictionHorizon(int predictionHorizon)

Setter for predictionHorizon

Parameters:

predictionHorizon - Number of samples into the future to predict.

getSupervisedLearner

public SupervisedBatchLearner<InputType,OutputType,EvaluatorType> getSupervisedLearner()

Getter for supervisedLearner

Returns:

Learning algorithm that does the heavy lifting.

setSupervisedLearner

public void setSupervisedLearner(SupervisedBatchLearner<InputType,OutputType,EvaluatorType> supervisedLearner)

Setter for supervisedLearner

Parameters:

supervisedLearner - Learning algorithm that does the heavy lifting.

learn

public EvaluatorType learn(java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data)

Description copied from interface: BatchLearner

The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Specified by:

learn in interface BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>

Parameters:

data - The data that the learning algorithm will use to create an object of ResultType.

Returns:

The object that is created based on the given data using the learning algorithm.

createPredictionDataset

public static <InputType,OutputType> java.util.ArrayList<InputOutputPair<InputType,OutputType>> createPredictionDataset(int predictionHorizon,
                                                                                                                        java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>> data)

Creates a dataset that can be used to predict the future by "predictionHorizon" samples. This is done by lagging the inputs to align with those into the future. Thus, the resulting Collection will have a size of "predictionHorizon" shorter than the given "data".

Type Parameters:

InputType - Type of inputs to the prediction engine.

OutputType - Type of outputs to predict from the prediction engine.

Parameters:

predictionHorizon - Number of samples into the future to predict.

data - Data to align for predicting into the future.

Returns:

Dataset, aligned to predict into the future, that is "predictionHorizon" samples shorter than the given "data".