  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.
