Making Time-series Classification More Accurate Using Learned Constraints

Abstract

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2004 SIAM International Conference on Data Mining (SDM)Making Time-series Classification More Accurate Using Learned ConstraintsChotirat Ann Ratanamahatana and Eamonn KeoghChotirat Ann Ratanamahatana and Eamonn Keoghpp.11 - 22Chapter DOI:https://doi.org/10.1137/1.9781611972740.2PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract 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. Previous chapter Next chapter RelatedDetails Published:2004ISBN:978-0-89871-568-2eISBN:978-1-61197-274-0 https://doi.org/10.1137/1.9781611972740Book Series Name:ProceedingsBook Code:PR117Book Pages:xiv + 537Key words:Time Series, Dynamic Time Warping