LoCoMotif: Discovering time-warped motifs in time series
Daan Van Wesenbeeck Aras Yurtman Wannes Meert Hendrik Blockeel
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.