Time Series Motif Discovery: A Comprehensive Evaluation
Summary: Process-centric taxonomy grouping 55 motif discovery methods into three families, formalizing key data challenges (amplitude/offset deformation, time-warped and variable-length occurrences, multi-scale motifs). Benchmark of 11 representative algorithms on real and synthetic labeled datasets (F1, runtime), providing practical algorithm-selection guidance and robustness insights. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Valerio Guerrini
- 2. Thibaut Germain
- 3. Charles Truong
- 4. Laurent Oudre
- 5. Paul Boniol
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 243 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases | 2001 | SIGMOD | 0.00031074984 |
| 1,253 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB | 0.00013032074 |
| 1,516 | k-Shape: Efficient and Accurate Clustering of Time Series | 2015 | SIGMOD | 0.00011586255 |
| 2,381 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB | 8.9327638e-05 |
| 4,219 | Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series | 2018 | SIGMOD | 6.3500768e-05 |
| 5,245 | Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees | 2022 | VLDB | 5.6067361e-05 |
| 6,687 | Motiflets - Simple and Accurate Detection of Motifs in Time Series | 2023 | VLDB | 4.9623586e-05 |
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