Optimal Time-Series Motifs

TL;DR

This paper introduces a novel optimization-based approach for discovering time-series motifs by learning parameters that maximize pattern frequency, outperforming traditional search-based methods in identifying the most repetitive patterns.

Contribution

It proposes a new principled optimization method for motif discovery that finds truly optimal motifs, even when they do not explicitly appear as sub-sequences.

Findings

Motifs learned by the new method are significantly more frequent than those found by search-based methods.

The approach can identify the most repetitive patterns even if they do not explicitly occur as sub-sequences.

Experiments on real datasets validate the effectiveness of the optimization approach.

Abstract

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.