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)
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 |
|---|---|---|---|---|
| 7,421 | Raising the ClaSS of Streaming Time Series Segmentation | 2024 | VLDB | 5.6225905e-05 |
| 9,302 | ISSD: Indicator Selection for Time Series State Detection | 2025 | SIGMOD | 5.289545e-05 |
| 9,311 | Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data | 2023 | SIGMOD | 5.289545e-05 |
| 10,069 | Mining and Forecasting of Big Time-series Data | 2015 | SIGMOD | 5.1643809e-05 |
| 10,607 | CLaP - State Detection from Time Series | 2026 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 31 | BIRCH: An Efficient Data Clustering Method for Very Large Databases | 1996 | SIGMOD | 0.00050347119 |
| 303 | On The Marriage of Lp-norms and Edit Distance | 2004 | VLDB | 0.00021956234 |
| 891 | Adaptive Stream Resource Management Using Kalman Filters | 2004 | SIGMOD | 0.00013382575 |
| 918 | Trajectory Clustering: A Partition-and-Group Framework | 2007 | SIGMOD | 0.00013215285 |
| 1,252 | Streaming Pattern Discovery in Multiple Time-Series | 2005 | VLDB | 0.00011483752 |
| 3,391 | BRAID: Stream Mining through Group Lag Correlations | 2005 | SIGMOD | 7.4518238e-05 |
| 3,394 | Prediction and Indexing of Moving Objects with Unknown Motion Patterns | 2004 | SIGMOD | 7.4500025e-05 |
| 3,399 | Finding Semantics in Time Series | 2011 | SIGMOD | 7.4466526e-05 |
| 5,834 | Leveraging Spatio-Temporal Redundancy for RFID Data Cleansing | 2010 | SIGMOD | 6.0721842e-05 |
| 6,205 | Outlier-robust Clustering using Independent Components | 2008 | SIGMOD | 5.9443504e-05 |
| 12,471 | Parsimonious Linear Fingerprinting for Time Series | 2010 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 13,318 | Fully Automated Correlated Time Series Forecasting in Minutes | 2025 | VLDB |
| 2 | 886 | Efficiently Mining Long Patterns from Databases | 1998 | SIGMOD |
| 3 | 10,859 | Time Series Motif Discovery: A Comprehensive Evaluation | 2025 | VLDB |
| 4 | 5,266 | Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees | 2022 | VLDB |
| 5 | 7,691 | Finding Relevant Patterns in Bursty Sequences | 2008 | VLDB |
| 6 | 4,141 | AutoAI-TS: AutoAI for Time Series Forecasting | 2021 | SIGMOD |
| 7 | 5,043 | Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information | 2022 | VLDB |
| 8 | 10,069 | Mining and Forecasting of Big Time-series Data | 2015 | SIGMOD |
| 9 | 12,471 | Parsimonious Linear Fingerprinting for Time Series | 2010 | VLDB |
| 10 | 10,927 | MOMENTI: Scalable Motif Mining in Multidimensional Time Series | 2025 | VLDB |