TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
Summary: TAB: a unified time-series anomaly-detection benchmark aggregating 29 public multivariate datasets and 1,635 univariate series and integrating non-learning, ML, deep-learning, LLM-based and pre-trained time-series methods. Includes an automated, standardized evaluation pipeline for fair, reproducible comparison and reports comprehensive cross-method performance analyses. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiangfei Qiu (East China Normal University)
- 2. Zhe Li (East China Normal University)
- 3. Wanghui Qiu (East China Normal University)
- 4. Shiyan Hu (East China Normal University)
- 5. Lekui Zhou (Huawei)
- 6. Xingjian Wu (East China Normal University)
- 7. Zhengyu Li (East China Normal University)
- 8. Chenjuan Guo (East China Normal University)
- 9. Aoying Zhou (East China Normal University)
- 10. Zhenli Sheng (Huawei)
- 11. Jilin Hu (East China Normal University)
- 12. Christian S. Jensen (Aalborg University)
- 13. Bin Yang (East China Normal University)
BibTeX Citation
@article{qiu_vldb25,
title = {{TAB: Unified Benchmarking of Time Series Anomaly Detection Methods}},
author = {Qiu, Xiangfei and Li, Zhe and Qiu, Wanghui and Hu, Shiyan and Zhou, Lekui and Wu, Xingjian and Li, Zhengyu and Guo, Chenjuan and Zhou, Aoying and Sheng, Zhenli and Hu, Jilin and Jensen, Christian S. and Yang, Bin},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {9},
pages = {2775--2789},
doi = {10.14778/3746405.3746407},
url = {https://doi.org/10.14778/3746405.3746407},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,298 | The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis] | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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