Making Time-series Classification More Accurate Using Learned Constraints

Making Time-series Classification More Accurate Using Learned Constraints (2004)

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by Chotirat Ann Ratanamahatana , Eamonn Keogh

Venue:	In proc. of SDM Int’l Conf
Citations:	42 - 13 self

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Datum	Value	Source
TITLE	Making Time-series Classification More Accurate Using Learned Constraints	SVM HeaderParse 0.2
AUTHOR NAME	Chotirat Ann Ratanamahatana	SVM HeaderParse 0.2
AUTHOR AFFIL	University of California- Riverside; Computer Science & Engineering Department	SVM HeaderParse 0.2
AUTHOR ADDR	Riverside, CA 92521, USA	SVM HeaderParse 0.2
AUTHOR NAME	Eamonn Keogh	SVM HeaderParse 0.2
AUTHOR AFFIL	University of California- Riverside; Computer Science & Engineering Department	SVM HeaderParse 0.2
AUTHOR ADDR	Riverside, CA 92521, USA	SVM HeaderParse 0.2
ABSTRACT	It has long been known that Dynamic Time Warping (DTW) is superior to Euclidean distance for classification and clustering of time series. However, until lately, most research has utilized Euclidean distance because it is more efficiently calculated. A recently introduced technique that greatly mitigates DTWs demanding CPU time has sparked a flurry of research activity. However, the technique and its many extensions still only allow DTW to be applied to moderately large datasets. In addition, almost all of the research on DTW has focused exclusively on speeding up its calculation; there has been little work done on improving its accuracy. In this work, we target the accuracy aspect of DTW performance and introduce a new framework that learns arbitrary constraints on the warping path of the DTW calculation. Apart from improving the accuracy of classification, our technique as a side effect speeds up DTW by a wide margin as well. We show the utility of our approach on datasets from diverse domains and demonstrate significant gains in accuracy and efficiency.	SVM HeaderParse 0.2
YEAR	2004	INFERENCE
VENUE	In proc. of SDM Int’l Conf	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	11--22	INFERENCE
CITATIONS	46 found	ParsCit 1.0