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

Aras Yurtman, Daan Van Wesenbeeck, Wannes Meert, Hendrik Blockeel
2/17/2025
cs.LGcs.AIcs.CV

No official code found. Best implementation: ML-KULeuven/locomotif_dok (4 stars, last commit 2025-09-08).

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.

View on arXivView PDF

Code Implementations(1)

ML-KULeuven/locomotif_dokCommunity100%

LoCoMotif-DoK is a time series motif discovery method that leverages domain knowledge represented by hard and soft constraints. It discovers variable-length motif sets in a multivariate time series using time warping.

40Python, Jupyter NotebookFeb 17, 20251 years agoNOASSERTION
constrained-optimizationdomain-knowledgemotif-discoverymultivariate-timeseriespattern-mining+4 more

Similar Papers

Quantitative Evaluation of Motif Sets in Time Series

80% match1 repo2024

LoCoMotif: Discovering time-warped motifs in time series

80% match2023

Framework for Variable-lag Motif Following Relation Inference In Time Series using Matrix Profile analysis

78% match1 repo32024

Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift

78% match1 repo712024

BRIDGE: Bootstrapping Text to Control Time-Series Generation via Multi-Agent Iterative Optimization and Diffusion Modelling

78% match2026

An Unsupervised Approach for Periodic Source Detection in Time Series

77% match1 repo142024

Cite this paper

@article{yurtman2025steering,
  title  = {Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery},
  author = {Aras Yurtman and Daan Van Wesenbeeck and Wannes Meert and Hendrik Blockeel},
  year   = {2025},
  eprint = {2502.11850},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.11850v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2502.11850.svg)](https://codeofpaper.com/paper/2502.11850)

Discussion