Probabilistic discovery of time series motifs

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Probabilistic discovery of time series motifs (2003)

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by Bill Chiu , Eamonn Keogh , Stefano Lonardi

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185 - 26 self

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@INPROCEEDINGS{Chiu03probabilisticdiscovery,
    author = {Bill Chiu and Eamonn Keogh and Stefano Lonardi},
    title = {Probabilistic discovery of time series motifs},
    booktitle = {},
    year = {2003},
    pages = {493--498}
}

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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.

Keyphrases

time series motif probabilistic discovery poor scalability anytime algorithm algorithm fast motif discovery algorithm time series recent advance pattern discovery likely candidate motif high probability care symbol novel algorithm core task

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