TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection
Summary: TSB-AutoAD: taxonomy plus benchmark (20 methods, 70 variants) for automated time-series anomaly detection across nine domains, organizing approaches into selection, ensembling, and generation. Extensive evaluation shows many methods fail to beat random; naive ensembling is accurate but costly, dataset-driven methods break OOD; proposes selective ensembling to balance accuracy and efficiency. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Qinghua Liu (Ohio State University)
- 2. Seunghak Lee (Meta)
- 3. John Paparrizos (Ohio State University)
BibTeX Citation
@article{liu_vldb25,
title = {{TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection}},
author = {Liu, Qinghua and Lee, Seunghak and Paparrizos, John},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {4364--4379},
doi = {10.14778/3749646.3749699},
url = {https://doi.org/10.14778/3749646.3749699},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,483 | Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression | 2025 | VLDB | 5.1708619e-05 |
| 10,469 | HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series | 2026 | SIGMOD | 4.9793485e-05 |
| 10,483 | MUFASA: Fast and Accurate Multivariate Time-Series Clustering | 2026 | SIGMOD | 4.9793485e-05 |
| 10,510 | The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis] | 2026 | SIGMOD | 4.9793485e-05 |
| 10,760 | KDSelector: A Framework of Knowledge-Enhanced and Data-Efficient Selector Learning for Anomaly Detection Model Selection in Time Series | 2026 | VLDB | 4.9793485e-05 |
| 11,348 | BURST: Rendering Clustering Techniques Suitable for Evolving Streams | 2025 | VLDB | 4.9793485e-05 |
| 11,419 | EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,115 | TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms | 2022 | VLDB |
| 2 | 4,185 | AutoAI-TS: AutoAI for Time Series Forecasting | 2021 | SIGMOD |
| 3 | 9,488 | Time-Series Anomaly Detection: Overview and New Trends | 2024 | VLDB |
| 4 | 1,005 | Anomaly Detection in Time Series: A Comprehensive Evaluation | 2022 | VLDB |
| 5 | 6,268 | An Experimental Evaluation of Anomaly Detection in Time Series | 2024 | VLDB |
| 6 | 9,567 | TAB: Unified Benchmarking of Time Series Anomaly Detection Methods | 2025 | VLDB |
| 7 | 3,726 | Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series | 2023 | VLDB |
| 8 | 11,419 | EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection | 2025 | VLDB |
| 9 | 6,073 | AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data | 2024 | VLDB |
| 10 | 1,937 | TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection | 2022 | VLDB |