A genetic algorithm to discover flexible motifs with support
This work addresses the open issue of motif discovery in time series analysis for researchers and practitioners, though it appears incremental as it builds on existing genetic algorithm approaches with added flexibility.
The paper tackles the problem of defining and discovering motifs in time series data by introducing a revised notion of motif support and proposing GENMOTIF, a genetic algorithm that flexibly accommodates various specifications and task characteristics, demonstrating its value in synthetic and real-world datasets like traffic volume and accelerometer signals.
Finding repeated patterns or motifs in a time series is an important unsupervised task that has still a number of open issues, starting by the definition of motif. In this paper, we revise the notion of motif support, characterizing it as the number of patterns or repetitions that define a motif. We then propose GENMOTIF, a genetic algorithm to discover motifs with support which, at the same time, is flexible enough to accommodate other motif specifications and task characteristics. GENMOTIF is an anytime algorithm that easily adapts to many situations: searching in a range of segment lengths, applying uniform scaling, dealing with multiple dimensions, using different similarity and grouping criteria, etc. GENMOTIF is also parameter-friendly: it has only two intuitive parameters which, if set within reasonable bounds, do not substantially affect its performance. We demonstrate the value of our approach in a number of synthetic and real-world settings, considering traffic volume measurements, accelerometer signals, and telephone call records.