Optimal Time-Series Motifs
This work addresses a fundamental issue for practitioners in time-series analysis by providing a more effective way to identify repetitive patterns, potentially improving interpretation in fields like finance or healthcare.
The paper tackles the problem of finding the most frequent patterns (motifs) in time-series data by proposing an optimization approach that learns optimal motifs, rather than searching among sub-sequences. Experiments on real-life datasets show that this method discovers motifs with significantly higher frequency than search-based methods for the same distance threshold.
Motifs are the most repetitive/frequent patterns of a time-series. The discovery of motifs is crucial for practitioners in order to understand and interpret the phenomena occurring in sequential data. Currently, motifs are searched among series sub-sequences, aiming at selecting the most frequently occurring ones. Search-based methods, which try out series sub-sequence as motif candidates, are currently believed to be the best methods in finding the most frequent patterns. However, this paper proposes an entirely new perspective in finding motifs. We demonstrate that searching is non-optimal since the domain of motifs is restricted, and instead we propose a principled optimization approach able to find optimal motifs. We treat the occurrence frequency as a function and time-series motifs as its parameters, therefore we \textit{learn} the optimal motifs that maximize the frequency function. In contrast to searching, our method is able to discover the most repetitive patterns (hence optimal), even in cases where they do not explicitly occur as sub-sequences. Experiments on several real-life time-series datasets show that the motifs found by our method are highly more frequent than the ones found through searching, for exactly the same distance threshold.