AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data

Summary: AutoTSAD unsupervisedly ensembles diverse subsequence detectors to produce anomaly scores for time series without training data. Automated configuration and ensembling match tuned detectors and beat prior selection baselines across diverse anomaly types. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13705
Venue: VLDB
Year: 2024
Pagerank: 6.0183734e-05
Overall Rank: 5,986 | 58.94%
DOI: 10.14778/3681954.3681978

Incoming Non-self Citations Over Time

Authors

1. Sebastian Schmidl (Hasso Plattner Institute; University of Potsdam)
2. Felix Naumann (Hasso Plattner Institute; University of Potsdam)
3. Thorsten Papenbrock (Philipps University Marburg)

BibTeX Citation

@article{schmidl_vldb24,
        title = {{AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data}},
        author = {Schmidl, Sebastian and Naumann, Felix and Papenbrock, Thorsten},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {11},
        pages = {2987--3002},
        doi = {10.14778/3681954.3681978},
        url = {https://doi.org/10.14778/3681954.3681978},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 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
10,977	TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection	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 11 of 11 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
1,534	Exathlon: A Benchmark for Explainable Anomaly Detection over Time Series	2021	VLDB	0.00010464308
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
3,447	Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection	2022	VLDB	7.4085215e-05
3,987	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB	6.9722766e-05
4,851	Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles	2022	VLDB	6.4803317e-05
6,141	An Experimental Evaluation of Anomaly Detection in Time Series	2024	VLDB	5.9628456e-05
6,356	TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms	2022	VLDB	5.9024453e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	9,379	Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains	2025	VLDB
2	1,793	TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data	2022	VLDB
3	4,851	Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles	2022	VLDB
4	11,056	EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection	2025	VLDB
5	11,300	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB
6	2,004	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB
7	9,382	TAB: Unified Benchmarking of Time Series Anomaly Detection Methods	2025	VLDB
8	6,141	An Experimental Evaluation of Anomaly Detection in Time Series	2024	VLDB
9	1,029	Anomaly Detection in Time Series: A Comprehensive Evaluation	2022	VLDB
10	10,977	TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection	2025	VLDB