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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)

Paper ID
13999
Venue
VLDB
Year
2025
Pagerank
5.2415551e-05
Overall Rank
5,991 | 58.33%
DOI
10.14778/3705829.3705852

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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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