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: h98a9b8055c9fe4f3
Venue: SIGMOD
Year: 2014
Pagerank: 6.5810269e-05
Overall Rank: 4,475 | 69.93%
DOI: 10.1145/2588555.2588556

Incoming Non-self Citations Over Time

Authors

1. Yasuko Matsubara (Kumamoto University)
2. Yasushi Sakurai (Kumamoto University)
3. Christos Faloutsos (Carnegie Mellon University)

BibTeX Citation

@inproceedings{matsubara_sigmod14,
        title = {{AutoPlait: Automatic Mining of Co-evolving Time Sequences}},
        author = {Matsubara, Yasuko and Sakurai, Yasushi and Faloutsos, Christos},
        series = {{SIGMOD} '14},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2588555.2588556},
        url = {https://dl.acm.org/doi/10.1145/2588555.2588556},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,565	Raising the ClaSS of Streaming Time Series Segmentation	2024	VLDB	5.4938327e-05
9,487	ISSD: Indicator Selection for Time Series State Detection	2025	SIGMOD	5.168414e-05
9,500	Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data	2023	SIGMOD	5.168414e-05
10,283	Mining and Forecasting of Big Time-series Data	2015	SIGMOD	5.0461162e-05
11,063	CLaP - State Detection from Time Series	2026	VLDB	4.9769913e-05

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.

Rank	Cited Paper	Year	Venue	Pagerank
32	BIRCH: An Efficient Data Clustering Method for Very Large Databases	1996	SIGMOD	0.00049714561
306	On The Marriage of Lp-norms and Edit Distance	2004	VLDB	0.00021565993
912	Adaptive Stream Resource Management Using Kalman Filters	2004	SIGMOD	0.0001310985
917	Trajectory Clustering: A Partition-and-Group Framework	2007	SIGMOD	0.00013085176
1,279	Streaming Pattern Discovery in Multiple Time-Series	2005	VLDB	0.00011226666
3,434	Finding Semantics in Time Series	2011	SIGMOD	7.2993169e-05
3,452	BRAID: Stream Mining through Group Lag Correlations	2005	SIGMOD	7.2873054e-05
3,463	Prediction and Indexing of Moving Objects with Unknown Motion Patterns	2004	SIGMOD	7.2799688e-05
5,940	Leveraging Spatio-Temporal Redundancy for RFID Data Cleansing	2010	SIGMOD	5.9378083e-05
6,336	Outlier-robust Clustering using Independent Components	2008	SIGMOD	5.8085039e-05
12,768	Parsimonious Linear Fingerprinting for Time Series	2010	VLDB	4.9769913e-05

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9	11,326	MOMENTI: Scalable Motif Mining in Multidimensional Time Series	2025	VLDB
10	12,768	Parsimonious Linear Fingerprinting for Time Series	2010	VLDB