LoCoMotif: Discovering time-warped motifs in time series
This addresses the problem of discovering flexible, time-warped motifs in time series for applications like physiotherapy, representing a novel method for a known bottleneck.
The paper tackles the limitations of existing Time Series Motif Discovery methods, such as fixed pattern lengths and inability to handle time variability, by introducing LoCoMotif, which outperforms existing methods and is demonstrated on a physiotherapy use case.
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.