Discovering Leitmotifs in Multidimensional Time Series
Summary: LAMA: an algorithm that jointly discovers leitmotifs in multidimensional time series and the unknown subset of relevant dimensions, addressing their interdependence rather than treating dimension selection and motif extraction separately. Balances “neither too few nor too many” sub-dimensions and outperforms four baselines on a 14‑dataset ground‑truth benchmark without added computational cost. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Patrick Schäfer
- 2. Ulf Leser
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,678 | MOMENTI: Scalable Motif Mining in Multidimensional Time Series | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,260 | VALMOD: A Suite for Easy and Exact Detection of Variable Length Motifs in Data Series | 2018 | SIGMOD | 9.1660405e-05 |
| 6,687 | Motiflets - Simple and Accurate Detection of Motifs in Time Series | 2023 | VLDB | 4.9623586e-05 |
| 6,797 | Raising the ClaSS of Streaming Time Series Segmentation | 2024 | VLDB | 4.9241565e-05 |
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| 10,678 | MOMENTI: Scalable Motif Mining in Multidimensional Time Series | 2025 | VLDB | 4.1945683e-05 |
| 6,687 | Motiflets - Simple and Accurate Detection of Motifs in Time Series | 2023 | VLDB | 4.9623586e-05 |
| 10,599 | Time Series Motif Discovery: A Comprehensive Evaluation | 2025 | VLDB | 4.1945683e-05 |