Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery

TL;DR

This paper introduces LoCoMotif-DoK, a framework and algorithm for incorporating domain knowledge into Time Series Motif Discovery, enabling more relevant motif identification in various applications.

Contribution

The paper presents a novel framework and efficient algorithm that allow user-defined constraints based on domain knowledge to improve motif discovery in time series data.

Findings

LoCoMotif-DoK outperforms existing TSMD methods in leveraging domain knowledge.

The framework effectively incorporates user constraints in real and synthetic datasets.

Experimental results show improved relevance of discovered motifs.

Abstract

Time Series Motif Discovery (TSMD) identifies repeating patterns in time series data, but its unsupervised nature might result in motifs that are not interesting to the user. To address this, we propose a framework that allows the user to impose constraints on the motifs to be discovered, where constraints can easily be defined according to the properties of the desired motifs in the application domain. We also propose an efficient implementation of the framework, the LoCoMotif-DoK algorithm. We demonstrate that LoCoMotif-DoK can effectively leverage domain knowledge in real and synthetic data, outperforming other TSMD techniques which only support a limited form of domain knowledge.