Cleaning Time Series under Seasonal and Trend Constraints
Summary: Enforce seasonal and trend constraints for time-series cleaning; show the constrained repair problem is NP‑complete and that standard decomposition is error-prone at boundaries. Offer an error‑tolerant, bidirectional seasonal-trend filter plus an iterative repair heuristic to refine constraints; deployed in Apache IoTDB with empirical improvements. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Zijie Chen (Tsinghua University)
- 2. Aoqian Zhang (Beijing Institute of Technology; Tangshan Research Institute)
- 3. Shaoxu Song (Tsinghua University)
BibTeX Citation
@inproceedings{chen_sigmod26,
title = {{Cleaning Time Series under Seasonal and Trend Constraints}},
author = {Chen, Zijie and Zhang, Aoqian and Song, Shaoxu},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769773},
url = {https://dl.acm.org/doi/10.1145/3769773},
year = {2026}
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 112 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB | 0.00032801121 |
| 1,029 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB | 0.00012557065 |
| 2,995 | Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing | 2017 | VLDB | 7.8750141e-05 |
| 3,793 | Apache IoTDB: A Time Series Database for IoT Applications | 2023 | SIGMOD | 7.1217835e-05 |
| 3,861 | RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection | 2021 | SIGMOD | 7.0666117e-05 |
| 4,345 | ASAP: Prioritizing Attention via Time Series Smoothing | 2017 | VLDB | 6.7513816e-05 |
| 4,994 | Sequential Data Cleaning: A Statistical Approach | 2016 | SIGMOD | 6.4092706e-05 |
| 6,073 | OnlineSTL: Scaling Time Series Decomposition by 100x | 2022 | VLDB | 5.9862575e-05 |
| 6,186 | SCREEN: Stream Data Cleaning under Speed Constraints | 2015 | SIGMOD | 5.9487817e-05 |
| 8,517 | OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting | 2023 | VLDB | 5.4119882e-05 |
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