DTW-D: time series semi-supervised learning from a single example

Authors:
Yanping Chen;Bing Hu;Eamonn Keogh;Gustavo E.A.P.A Batista
Affiliations:
University of California, Riverside, Riverside, USA;University of California, Riverside, Riverside, USA;University of California, Riverside, Riverside, USA;Universidade de São Paulo - USP, São Paulo, Brazil
Venue:
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2013

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Abstract

Classification of time series data is an important problem with applications in virtually every scientific endeavor. The large research community working on time series classification has typically used the UCR Archive to test their algorithms. In this work we argue that the availability of this resource has isolated much of the research community from the following reality, labeled time series data is often very difficult to obtain. The obvious solution to this problem is the application of semi-supervised learning; however, as we shall show, direct applications of off-the-shelf semi-supervised learning algorithms do not typically work well for time series. In this work we explain why semi-supervised learning algorithms typically fail for time series problems, and we introduce a simple but very effective fix. We demonstrate our ideas on diverse real word problems.