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arXiv · 2311.17582

LoCoMotif: Discovering time-warped motifs in time series

Daan Van Wesenbeeck·Aras Yurtman·Wannes Meert·Hendrik Blockeel
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Abstract

Time Series Motif Discovery (TSMD) refers to the task of identifying patterns that occur multiple times (possibly with minor variations) in a time series. All existing methods for TSMD have one or more of the following limitations: they only look for the two most similar occurrences of a pattern; they only look for patterns of a pre-specified, fixed length; they cannot handle variability along the time axis; and they only handle univariate time series. In this paper, we present a new method, LoCoMotif, that has none of these limitations. The method is motivated by a concrete use case from physiotherapy. We demonstrate the value of the proposed method on this use case. We also introduce a new quantitative evaluation metric for motif discovery, and benchmark data for comparing TSMD methods. LoCoMotif substantially outperforms the existing methods, on top of being more broadly applicable.

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Daan Van Wesenbeeck, Aras Yurtman, Wannes Meert, Hendrik Blockeel. 2023-11-29. LoCoMotif: Discovering time-warped motifs in time series. https://arxiv.org/abs/2311.17582

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