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AutoPlait: Automatic Mining of Co-evolving Time Sequences

Summary: AutoPlait automatically mines co-evolving time sequences with unknown pattern counts and diverse durations. Parameter-free, linear-scaling, no training or tuning, it detects similar segment groups and segments sequences; outperforms peers in precision/recall (>95%) and speed (up to 472x) on 67GB of real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4761
Venue
SIGMOD
Year
2014
Pagerank
6.4819215e-05
Overall Rank
4,065 | 71.73%
DOI
10.1145/2588555.2588556

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
6,797 Raising the ClaSS of Streaming Time Series Segmentation 2024 VLDB 4.9241565e-05
9,147 ISSD: Indicator Selection for Time Series State Detection 2025 SIGMOD 4.3849295e-05
9,156 Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data 2023 SIGMOD 4.3849295e-05
9,920 Mining and Forecasting of Big Time-series Data 2015 SIGMOD 4.2561557e-05
10,309 CLaP - State Detection from Time Series 2026 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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