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
- 2. Aoqian Zhang
- 3. Shaoxu Song
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 192 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB | 0.00035728858 |
| 1,253 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB | 0.00013032074 |
| 3,133 | Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing | 2017 | VLDB | 7.4978041e-05 |
| 3,967 | Apache IoTDB: A Time Series Database for IoT Applications | 2023 | SIGMOD | 6.5796647e-05 |
| 4,113 | RobustPeriod: Robust Time-Frequency Mining for Multiple Periodicity Detection | 2021 | SIGMOD | 6.4420064e-05 |
| 4,420 | ASAP: Prioritizing Attention via Time Series Smoothing | 2017 | VLDB | 6.2011459e-05 |
| 5,002 | Sequential Data Cleaning: A Statistical Approach | 2016 | SIGMOD | 5.7671075e-05 |
| 6,204 | OnlineSTL: Scaling Time Series Decomposition by 100x | 2022 | VLDB | 5.1590612e-05 |
| 6,583 | SCREEN: Stream Data Cleaning under Speed Constraints | 2015 | SIGMOD | 5.0027988e-05 |
| 8,286 | OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting | 2023 | VLDB | 4.5435639e-05 |
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