# Quantitative Evaluation of Motif Sets in Time Series

arXiv: https://arxiv.org/abs/2412.09346 · HTML: https://mlsanity.com/paper/2412.09346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [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)
- [LoCoMotif: Discovering time-warped motifs in time series](https://mlsanity.com/paper/2311.17582/) (2023)

## Built on by (most cited first, 1 of 1)

- [Steering the LoCoMotif: Using Domain Knowledge in Time Series Motif Discovery](https://mlsanity.com/paper/2502.11850/) (2025)

## Abstract (arXiv)

Time Series Motif Discovery (TSMD), which aims at finding recurring patterns in time series, is an important task in numerous application domains, and many methods for this task exist. These methods are usually evaluated qualitatively. A few metrics for quantitative evaluation, where discovered motifs are compared to some ground truth, have been proposed, but they typically make implicit assumptions that limit their applicability. This paper introduces PROM, a broadly applicable metric that overcomes those limitations, and TSMD-Bench, a benchmark for quantitative evaluation of time series motif discovery. Experiments with PROM and TSMD-Bench show that PROM provides a more comprehensive evaluation than existing metrics, that TSMD-Bench is a more challenging benchmark than earlier ones, and that the combination can help understand the relative performance of TSMD methods. More generally, the proposed approach enables large-scale, systematic performance comparisons in this field.