SHoTClean: Bridging Soft and Hard Constraints for Multivariate Time Series Cleaning
Summary: SHoTClean reframes multivariate time-series cleaning as constrained optimization: hard physical bounds + soft statistical consistency, targeting subtle dirty points missed by prior univariate-centric methods. Offers offline/streaming algorithms (pruned/incremental DP, near-linear acceleration) and a causal variant to model inter-variable dependencies. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Ziquan Fang (Zhejiang University)
- 2. Wei Shao (Zhejiang University)
- 3. Zheqi Lu (Zhejiang University)
- 4. Lu Chen (Zhejiang University)
- 5. Yunjun Gao (Zhejiang University)
BibTeX Citation
@inproceedings{fang_sigmod26,
title = {{SHoTClean: Bridging Soft and Hard Constraints for Multivariate Time Series Cleaning}},
author = {Fang, Ziquan and Shao, Wei and Lu, Zheqi and Chen, Lu and Gao, Yunjun},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786698},
url = {https://dl.acm.org/doi/10.1145/3786698},
year = {2026}
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