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Making Time-Series Classification More Accurate Using Learned Constraints

11 years 11 months ago
Making Time-Series Classification More Accurate Using Learned Constraints
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 dive...
Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where SDM
Authors Chotirat (Ann) Ratanamahatana, Eamonn J. Keogh
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