Time Series Motif Discovery: A Comprehensive Evaluation
Summary: Comprehensive review and empirical comparison of time-series motif discovery under diverse data challenges and motif definitions; distills algorithmic strengths, limitations, selection guidelines, and open research directions. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Valerio Guerrini (CNRS; Centre Borelli; French Armed Forces Health Service; French National Institute of Health and Medical Research; University of Paris; Université Paris Cité; École normale supérieure Paris-Saclay)
- 2. Thibaut Germain (CNRS; Centre Borelli; French Armed Forces Health Service; French National Institute of Health and Medical Research; University of Paris; Université Paris Cité; École normale supérieure Paris-Saclay)
- 3. Charles Truong (CNRS; Centre Borelli; French Armed Forces Health Service; French National Institute of Health and Medical Research; University of Paris; Université Paris Cité; École normale supérieure Paris-Saclay)
- 4. Laurent Oudre (CNRS; Centre Borelli; French Armed Forces Health Service; French National Institute of Health and Medical Research; University of Paris; Université Paris Cité; École normale supérieure Paris-Saclay)
- 5. Paul Boniol (CNRS; Department of Computer Science of École normale supérieure; INRIA; Paris Sciences et Lettres University; École normale supérieure)
BibTeX Citation
@article{guerrini_vldb25,
title = {{Time Series Motif Discovery: A Comprehensive Evaluation}},
author = {Guerrini, Valerio and Germain, Thibaut and Truong, Charles and Oudre, Laurent and Boniol, Paul},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {7},
pages = {2226--2239},
doi = {10.14778/3734839.3734857},
url = {https://doi.org/10.14778/3734839.3734857},
year = {2025}
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Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 190 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases | 2001 | SIGMOD | 0.00026105472 |
| 1,029 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB | 0.00012557065 |
| 1,579 | k-Shape: Efficient and Accurate Clustering of Time Series | 2015 | SIGMOD | 0.00010305183 |
| 2,004 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 9.3207067e-05 |
| 4,506 | Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series | 2018 | SIGMOD | 6.6569729e-05 |
| 5,266 | Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees | 2022 | VLDB | 6.2939621e-05 |
| 6,784 | Motiflets - Simple and Accurate Detection of Motifs in Time Series | 2023 | VLDB | 5.7758194e-05 |
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