Probabilistic discovery of time series motifs

Authors:
Bill Chiu;Eamonn Keogh;Stefano Lonardi
Affiliations:
University of California - Riverside, Riverside, CA;University of California - Riverside, Riverside, CA;University of California - Riverside, Riverside, CA
Venue:
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2003

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Abstract

Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of this work were the poor scalability of the motif discovery algorithm, and the inability to discover motifs in the presence of noise.Here we address these limitations by introducing a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences. Our algorithm is probabilistic in nature, but as we show empirically and theoretically, it can find time series motifs with very high probability even in the presence of noise or "don't care" symbols. Not only is the algorithm fast, but it is an anytime algorithm, producing likely candidate motifs almost immediately, and gradually improving the quality of results over time.