Querying and mining of time series data: Experimental comparison of representations and distance measures

Hui Ding*, Goce Trajcevski, Peter Scheuermann, Xiaoyue Wang, Eamonn Keogh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1080 Scopus citations


The last decade has witnessed a tremendous growths of interests in applications that deal with querying and mining of time series data. Numerous representation methods for dimensionality reduction and similarity measures geared towards time series have been introduced. 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 set of time series experiments re-implementing 8 different representation methods and 9 similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this paper, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. Our experiments have provided both a unified validation of some of the existing achievements, and in some cases, suggested that certain claims in the literature may be unduly optimistic.

Original languageEnglish (US)
Pages (from-to)1542-1552
Number of pages11
JournalProceedings of the VLDB Endowment
Issue number2
StatePublished - Aug 2008

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)


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