Motiflets - Simple and Accurate Detection of Motifs in Time Series
Summary: Defines k-Motiflets as exactly k length-l occurrences minimizing maximum pairwise distance, replacing hard-to-tune radius r with intuitive k. Exact/approximate search plus statistical parameter selection yields larger, more coherent motifs without manual tuning. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Patrick Schäfer (Humboldt-Universität zu Berlin)
- 2. Ulf Leser (Humboldt-Universität zu Berlin)
BibTeX Citation
@article{schafer_vldb23,
title = {{Motiflets - Simple and Accurate Detection of Motifs in Time Series}},
author = {Schäfer, Patrick and Leser, Ulf},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {4},
pages = {725--737},
doi = {10.14778/3574245.3574257},
url = {https://doi.org/10.14778/3574245.3574257},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,952 | Discovering Leitmotifs in Multidimensional Time Series | 2025 | VLDB | 5.7303405e-05 |
| 7,421 | Raising the ClaSS of Streaming Time Series Segmentation | 2024 | VLDB | 5.6225905e-05 |
| 10,607 | CLaP - State Detection from Time Series | 2026 | VLDB | 5.093636e-05 |
| 10,828 | TELESAFE: Detecting Private/Work Boundary Crossings in Energy Consumption Trails in Telework | 2025 | VLDB | 5.093636e-05 |
| 10,859 | Time Series Motif Discovery: A Comprehensive Evaluation | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,182 | VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series | 2018 | SIGMOD | 7.6589981e-05 |
| 4,506 | Matrix Profile X: VALMOD - Scalable Discovery of Variable-Length Motifs in Data Series | 2018 | SIGMOD | 6.6569729e-05 |
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