Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing
Summary: Proposes iterative minimum repairing (IMR) for time series, repairing anomalies via temporal context and the minimum-change principle. Convergence analysis and incremental O(1) per-iteration parameter estimation reduce cost from O(n) to O(1); real-data experiments show superior repair and improved time-series classification. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Aoqian Zhang (Tsinghua University)
- 2. Shaoxu Song (Tsinghua University)
- 3. Jianmin Wang (Tsinghua University)
- 4. Philip S. Yu (Tsinghua University; University of Illinois Chicago)
BibTeX Citation
@article{zhang_vldb17,
title = {{Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing}},
author = {Zhang, Aoqian and Song, Shaoxu and Wang, Jianmin and Yu, Philip S.},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {10},
doi = {10.14778/3115404.3115410},
url = {https://doi.org/10.14778/3115404.3115410},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 188 | A Cost-Based Model and Effective Heuristic for Repairing Constraints by Value Modification | 2005 | SIGMOD | 0.00025872962 |
| 938 | Truth Finding on the Deep Web: Is the Problem Solved? | 2013 | VLDB | 0.00012973266 |
| 989 | Towards Certain Fixes with Editing Rules and Master Data | 2010 | VLDB | 0.0001265344 |
| 1,360 | Adaptive Cleaning for RFID Data Streams | 2006 | VLDB | 0.00010918075 |
| 6,306 | SCREEN: Stream Data Cleaning under Speed Constraints | 2015 | SIGMOD | 5.8179005e-05 |
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