An Experimental Evaluation of Anomaly Detection in Time Series
Summary: Systematic benchmark of 17 time-series anomaly detectors across a taxonomy of data dimensions, techniques, and anomaly types. Evaluates effectiveness, efficiency, and robustness on real/synthetic data using point and subsequence-aware range metrics, yielding practical method-selection guidance. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Aoqian Zhang (Beijing Institute of Technology)
- 2. Shuqing Deng (Beijing Institute of Technology)
- 3. Dongping Cui (Beijing Institute of Technology)
- 4. Ye Yuan (Beijing Institute of Technology)
- 5. Guoren Wang (Beijing Institute of Technology)
BibTeX Citation
@article{zhang_vldb24,
title = {{An Experimental Evaluation of Anomaly Detection in Time Series}},
author = {Zhang, Aoqian and Deng, Shuqing and Cui, Dongping and Yuan, Ye and Wang, Guoren},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {3},
pages = {483--496},
doi = {10.14778/3632093.3632110},
url = {https://doi.org/10.14778/3632093.3632110},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,986 | AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data | 2024 | VLDB | 6.0183734e-05 |
| 9,382 | TAB: Unified Benchmarking of Time Series Anomaly Detection Methods | 2025 | VLDB | 5.2755515e-05 |
| 9,699 | MTSClean: Efficient Constraint-based Cleaning for Multi-Dimensional Time Series Data | 2024 | VLDB | 5.2351259e-05 |
| 10,843 | Streaming Time Series Subsequence Anomaly Detection: A Glance and Focus Approach | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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