LoCoMotif: Discovering time-warped motifs in time series

TL;DR

LoCoMotif is a novel method for time series motif discovery that overcomes key limitations of existing approaches, handling variable-length, time-warped, multivariate patterns with improved accuracy.

Contribution

It introduces LoCoMotif, a comprehensive TSMD method that addresses fixed length, univariate, and time-warping limitations, and provides a new evaluation metric and benchmark data.

Findings

LoCoMotif outperforms existing TSMD methods.

It handles multivariate and variable-length motifs.

The method is validated on physiotherapy data.

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.