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)

Paper ID: 14239
Venue: VLDB
Year: 2025
Pagerank: 5.093636e-05
Overall Rank: 10,977 | 24.69%
DOI: 10.14778/3749646.3749699

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

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 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,256	HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series	2026	SIGMOD	5.093636e-05
10,271	MUFASA: Fast and Accurate Multivariate Time-Series Clustering	2026	SIGMOD	5.093636e-05
10,298	The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis]	2026	SIGMOD	5.093636e-05
10,961	BURST: Rendering Clustering Techniques Suitable for Evolving Streams	2025	VLDB	5.093636e-05
10,980	Beyond Compression: A Comprehensive Evaluation of Lossless Floating-Point Compression	2025	VLDB	5.093636e-05
11,056	EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection	2025	VLDB	5.093636e-05

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.

Rank	Cited Paper	Year	Venue	Pagerank
142	LOF: Identifying Density-Based Local Outliers	2000	SIGMOD	0.0002962566
578	Efficient Algorithms for Mining Outliers from Large Data Sets	2000	SIGMOD	0.00016221871
1,029	Anomaly Detection in Time Series: A Comprehensive Evaluation	2022	VLDB	0.00012557065
1,579	k-Shape: Efficient and Accurate Clustering of Time Series	2015	SIGMOD	0.00010305183
1,805	SAND: Streaming Subsequence Anomaly Detection	2021	VLDB	9.7116108e-05
2,004	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	9.3207067e-05
2,023	Decomposed Bounded Floats for Fast Compression and Queries	2021	VLDB	9.2950046e-05
2,346	Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series	2020	VLDB	8.7168932e-05
3,447	Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection	2022	VLDB	7.4085215e-05
4,062	AutoOD: Automatic Outlier Detection	2023	SIGMOD	6.9309994e-05
4,234	GRAIL: Efficient Time-Series Representation Learning	2019	VLDB	6.8171563e-05
4,604	Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures	2020	SIGMOD	6.6104585e-05
5,622	Good to the Last Bit: Data-Driven Encoding with CodecDB	2021	SIGMOD	6.1461066e-05
5,986	AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data	2024	VLDB	6.0183734e-05
6,061	VergeDB: A Database for IoT Analytics on Edge Devices	2021	CIDR	5.9910733e-05
8,000	PIDS: Attribute Decomposition for Improved Compression and Query Performance in Columnar Storage	2020	VLDB	5.5092344e-05
9,448	Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection	2022	VLDB	5.2665425e-05
9,477	Odyssey: An Engine Enabling The Time-Series Clustering Journey	2023	VLDB	5.2634238e-05
10,747	A Structured Study of Multivariate Time-Series Distance Measures	2025	SIGMOD	5.093636e-05
10,978	Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods	2025	VLDB	5.093636e-05
11,056	EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection	2025	VLDB	5.093636e-05
11,435	Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances	2023	VLDB	5.093636e-05

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