# LoCoMotif: Discovering time-warped motifs in time series

arXiv: https://arxiv.org/abs/2311.17582 · HTML: https://mlsanity.com/paper/2311.17582/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Should we really use post-hoc tests based on mean-ranks?](https://mlsanity.com/paper/1505.02288/) (2015)
- [A Benchmark Study on Time Series Clustering](https://mlsanity.com/paper/2004.09546/) (2020)
- [Motiflets -- Simple and Accurate Detection of Motifs in Time Series](https://mlsanity.com/paper/2206.03735/) (2022)
- [Matrix Profile XXII: Exact Discovery of Time Series Motifs under DTW](https://mlsanity.com/paper/2009.07907/) (2020)
- [Finding Motif Sets in Time Series](https://mlsanity.com/paper/1407.3685/) (2014)
- [Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time Series](https://mlsanity.com/paper/1911.09218/) (2019)

## Built on by (most cited first, 3 of 3)

- [Quantitative Evaluation of Motif Sets in Time Series](https://mlsanity.com/paper/2412.09346/) (2024)
- [Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery](https://mlsanity.com/paper/2502.11850/) (2025)
- [Panache: One-Pass Motif Discovery at Every Window Length](https://mlsanity.com/paper/2607.17481/) (2026)

## Abstract (arXiv)

Time Series Motif Discovery (TSMD) refers to the task of identifying patterns that occur multiple times (possibly with minor variations) in a time series. All existing methods for TSMD have one or more of the following limitations: they only look for the two most similar occurrences of a pattern; they only look for patterns of a pre-specified, fixed length; they cannot handle variability along the time axis; and they only handle univariate time series. In this paper, we present a new method, LoCoMotif, that has none of these limitations. The method is motivated by a concrete use case from physiotherapy. We demonstrate the value of the proposed method on this use case. We also introduce a new quantitative evaluation metric for motif discovery, and benchmark data for comparing TSMD methods. LoCoMotif substantially outperforms the existing methods, on top of being more broadly applicable.