ai.libs.jaicore.ml.tsc.classifier.trees

Class TimeSeriesBagOfFeaturesClassifier

java.lang.Object

ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier<java.lang.Integer>

ai.libs.jaicore.ml.tsc.classifier.trees.TimeSeriesBagOfFeaturesClassifier

public class TimeSeriesBagOfFeaturesClassifier
extends ASimplifiedTSClassifier<java.lang.Integer>

Implementation of the Time Series Bag-of-Features (TSBF) classifier as described in Baydogan, Mustafa & Runger, George & Tuv, Eugene. (2013). A Bag-of-Features Framework to Classify Time Series. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35. 2796-802. 10.1109/TPAMI.2013.72. This classifier only supports univariate time series prediction.

Field Summary

Fields inherited from class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

classMapper, trained

Constructor Summary

Constructors

Constructor and Description
TimeSeriesBagOfFeaturesClassifier(int seed) Standard constructor using the default parameters (numBins = 10, numFolds = 10, zProp = 0.1, minIntervalLength = 5) for the TSBF classifier.
TimeSeriesBagOfFeaturesClassifier(int seed, int numBins, int numFolds, double zProp, int minIntervalLength) Constructor specifying parameters (cf.
TimeSeriesBagOfFeaturesClassifier(int seed, int numBins, int numFolds, double zProp, int minIntervalLength, boolean useZNormalization) Constructor specifying parameters (cf.

Method Summary

Modifier and Type	Method and Description
TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig	getConfig()
weka.classifiers.trees.RandomForest	getFinalClf()
int[][][]	getIntervals()
TimeSeriesBagOfFeaturesLearningAlgorithm	getLearningAlgorithm(TimeSeriesDataset dataset)
int	getNumBins()
int	getNumClasses()
int[][]	getSubsequences()
weka.classifiers.trees.RandomForest	getSubseriesClf()
java.lang.Integer	predict(double[] univInstance) Method predicting the class of the given univInstance.
java.lang.Integer	predict(java.util.List<double[]> multivInstance) Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.
java.util.List<java.lang.Integer>	predict(TimeSeriesDataset dataset) Performs predictions based on the given instances in the given dataset.
void	setFinalClf(weka.classifiers.trees.RandomForest finalClf)
void	setIntervals(int[][][] intervals)
void	setNumBins(int numBins)
void	setNumClasses(int numClasses)
void	setSubsequences(int[][] subsequences)
void	setSubseriesClf(weka.classifiers.trees.RandomForest subseriesClf)

Methods inherited from class ai.libs.jaicore.ml.tsc.classifier.ASimplifiedTSClassifier

checkWhetherPredictionIsPossible, getClassMapper, isTrained, setClassMapper, train

Methods inherited from class java.lang.Object

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

Constructor Detail

TimeSeriesBagOfFeaturesClassifier

public TimeSeriesBagOfFeaturesClassifier(int seed)

Standard constructor using the default parameters (numBins = 10, numFolds = 10, zProp = 0.1, minIntervalLength = 5) for the TSBF classifier.

Parameters:

seed - Seed used for randomized operations

TimeSeriesBagOfFeaturesClassifier

public TimeSeriesBagOfFeaturesClassifier(int seed,
                                         int numBins,
                                         int numFolds,
                                         double zProp,
                                         int minIntervalLength)

Constructor specifying parameters (cf. TimeSeriesBagOfFeaturesClassifier(int)).

Parameters:

seed - Seed used for randomized operations

numBins - See TimeSeriesBagOfFeaturesClassifier#numBins

numFolds - Number of folds for the internal OOB probability CV estimation

zProp - Proportion of the total time series length to be used for the subseries generation

minIntervalLength - The minimal interval length used for the interval generation

TimeSeriesBagOfFeaturesClassifier

public TimeSeriesBagOfFeaturesClassifier(int seed,
                                         int numBins,
                                         int numFolds,
                                         double zProp,
                                         int minIntervalLength,
                                         boolean useZNormalization)

Constructor specifying parameters (cf. TimeSeriesBagOfFeaturesClassifier(int)).

Parameters:

seed - Seed used for randomized operations

numBins - See TimeSeriesBagOfFeaturesClassifier#numBins

numFolds - Number of folds for the internal OOB probability CV estimation

zProp - Proportion of the total time series length to be used for the subseries generation

minIntervalLength - The minimal interval length used for the interval generation

useZNormalization - Indicator whether the Z normalization should be used

Method Detail

predict

public java.lang.Integer predict(double[] univInstance)
                          throws PredictionException

Method predicting the class of the given univInstance. At first, an internal feature representation using a bag of features is generated by the previously trained subsequences and intervals. These internal instances are used to get an internal class probability estimation for each subsequence and interval for each instance using a Random Forest classifier. These probabilities are aggregated to a histogram which is then fed to a final Random Forest classifier predicting the instance's target class.

Specified by:

predict in class ASimplifiedTSClassifier<java.lang.Integer>

Parameters:

univInstance - Univariate instance given by a double vector of time series values used for the prediction

Returns:

Returns the result of the prediction

Throws:

PredictionException - If something fails during the prediction process.

predict

public java.lang.Integer predict(java.util.List<double[]> multivInstance)
                          throws PredictionException

Performs a prediction based on the given multivariate list of double[] instance representation and returns the result.

Overrides:

predict in class ASimplifiedTSClassifier<java.lang.Integer>

Parameters:

multivInstance - Multivariate instance given by a list of multiple double[] time series used for the prediction

Returns:

Returns the result of the prediction

Throws:

PredictionException - If something fails during the prediction process.

predict

public java.util.List<java.lang.Integer> predict(TimeSeriesDataset dataset)
                                          throws PredictionException

Performs predictions based on the given instances in the given dataset.

Specified by:

predict in class ASimplifiedTSClassifier<java.lang.Integer>

Parameters:

dataset - The TimeSeriesDataset for which predictions should be made.

Returns:

Returns the result of the predictions

Throws:

PredictionException - If something fails during the prediction process

getSubseriesClf

public weka.classifiers.trees.RandomForest getSubseriesClf()

Returns:

the subseriesClf

setSubseriesClf

public void setSubseriesClf(weka.classifiers.trees.RandomForest subseriesClf)

Parameters:

subseriesClf - the subseriesClf to set

getFinalClf

public weka.classifiers.trees.RandomForest getFinalClf()

Returns:

the finalClf

setFinalClf

public void setFinalClf(weka.classifiers.trees.RandomForest finalClf)

Parameters:

finalClf - the finalClf to set

getNumBins

public int getNumBins()

Returns:

the numBins

setNumBins

public void setNumBins(int numBins)

Parameters:

numBins - the numBins to set

getNumClasses

public int getNumClasses()

Returns:

the numClasses

setNumClasses

public void setNumClasses(int numClasses)

Parameters:

numClasses - the numClasses to set

getIntervals

public int[][][] getIntervals()

Returns:

the intervals

setIntervals

public void setIntervals(int[][][] intervals)

Parameters:

intervals - the intervals to set

getSubsequences

public int[][] getSubsequences()

Returns:

the subsequences

setSubsequences

public void setSubsequences(int[][] subsequences)

Parameters:

subsequences - the subsequences to set

getLearningAlgorithm

public TimeSeriesBagOfFeaturesLearningAlgorithm getLearningAlgorithm(TimeSeriesDataset dataset)

Specified by:

getLearningAlgorithm in class ASimplifiedTSClassifier<java.lang.Integer>

getConfig

public TimeSeriesBagOfFeaturesLearningAlgorithm.ITimeSeriesBagOfFeaturesConfig getConfig()