Experimental comparison of representation methods and distance measures for time series data

  • Authors:

  • Xiaoyue Wang;Abdullah Mueen;Hui Ding;Goce Trajcevski;Peter Scheuermann;Eamonn Keogh

  • Affiliations:

  • University of California Riverside, Riverside, USA;University of California Riverside, Riverside, USA;Northwestern University, Evanston, USA;Northwestern University, Evanston, USA;Northwestern University, Evanston, USA;University of California Riverside, Riverside, USA

  • Venue:
  • Data Mining and Knowledge Discovery
  • Year:
  • 2013

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Abstract

The previous decade has brought a remarkable increase of the interest in applications that deal with querying and mining of time series data. Many of the research efforts in this context have focused on introducing new representation methods for dimensionality reduction or novel similarity measures for the underlying data. In the vast majority of cases, each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive experimental study re-implementing eight different time series representations and nine similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this article, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. In addition to providing a unified validation of some of the existing achievements, our experiments also indicate that, in some cases, certain claims in the literature may be unduly optimistic.