document: Probabilistic discovery of time series motifs 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 `` do n'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 .
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