MUFASA: Fast and Accurate Multivariate Time-Series Clustering
Summary: MUFASA extends k-Shape with SBD-D for global cross-channel alignment and a joint, efficient centroid update, capturing multivariate dependencies without cubic costs. It matches elastic-distance accuracy 2–4 orders faster and outperforms scalable and deep baselines. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Haojun Li (Ohio State University)
- 2. John Paparrizos (Ohio State University)
BibTeX Citation
@inproceedings{li_sigmod26,
title = {{MUFASA: Fast and Accurate Multivariate Time-Series Clustering}},
author = {Li, Haojun and Paparrizos, John},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802090},
url = {https://dl.acm.org/doi/10.1145/3802090},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,546 | HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series | 2026 | SIGMOD | 4.9605343e-05 |
| 10,587 | The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis] | 2026 | SIGMOD | 4.9605343e-05 |
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