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

arXiv: https://arxiv.org/abs/2502.11850 · HTML: https://mlsanity.com/paper/2502.11850/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Motiflets -- Simple and Accurate Detection of Motifs in Time Series](https://mlsanity.com/paper/2206.03735/) (2022)
- [LoCoMotif: Discovering time-warped motifs in time series](https://mlsanity.com/paper/2311.17582/) (2023)
- [Quantitative Evaluation of Motif Sets in Time Series](https://mlsanity.com/paper/2412.09346/) (2024)

## Abstract (arXiv)

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.