Anomaly Detection in Time Series: A Comprehensive Evaluation

Summary: Comprehensive, large-scale empirical study re-implements 71 anomaly detectors for time series. Evaluated on 976 datasets, analyzes effectiveness, efficiency, and robustness across families; provides guidance for detector selection and suggests future research directions. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h9aacc6f007c674db
Venue: VLDB
Year: 2022
Pagerank: 0.00012542496
Overall Rank: 1,007 | 93.26%
DOI: 10.14778/3538598.3538602
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

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

BibTeX Citation

@article{schmidl_vldb22,
        title = {{Anomaly Detection in Time Series: A Comprehensive Evaluation}},
        author = {Schmidl, Sebastian and Wenig, Phillip and Papenbrock, Thorsten},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {9},
        pages = {1779--1797},
        doi = {10.14778/3538598.3538602},
        url = {https://doi.org/10.14778/3538598.3538602},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 25 of 25 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,734	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB	7.0434227e-05
4,935	METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection	2024	VLDB	6.3343977e-05
5,138	TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms	2022	VLDB	6.2442621e-05
5,577	ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection	2024	VLDB	6.0619804e-05
6,090	AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data	2024	VLDB	5.8730318e-05
6,283	An Experimental Evaluation of Anomaly Detection in Time Series	2024	VLDB	5.8075726e-05
7,124	Time Series Data Mining: A Unifying View	2023	VLDB	5.580601e-05
7,272	Akane: Perplexity-Guided Time Series Data Cleaning	2024	SIGMOD	5.5530425e-05
7,592	Benchmarking the Utility of w-event Differential Privacy Mechanisms - When Baselines Become Mighty Competitors	2023	VLDB	5.4732818e-05
7,660	KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection	2025	SIGMOD	5.4565877e-05
8,049	TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications	2023	VLDB	5.3841015e-05
9,107	A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis	2024	VLDB	5.208561e-05
9,522	TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection	2025	VLDB	5.151324e-05
9,528	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB	5.151324e-05
9,597	Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains	2025	VLDB	5.1376962e-05
9,600	TAB: Unified Benchmarking of Time Series Anomaly Detection Methods	2025	VLDB	5.1376962e-05
9,912	Clean4TSDB: A Data Cleaning Tool for Time Series Databases	2024	VLDB	5.0983269e-05
10,546	HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series	2026	SIGMOD	4.9605343e-05
10,587	The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis]	2026	SIGMOD	4.9605343e-05
10,629	Cleaning Time Series under Seasonal and Trend Constraints	2026	SIGMOD	4.9605343e-05
10,831	KDSelector: A Framework of Knowledge-Enhanced and Data-Efficient Selector Learning for Anomaly Detection Model Selection in Time Series	2026	VLDB	4.9605343e-05
11,313	Streaming Time Series Subsequence Anomaly Detection: A Glance and Focus Approach	2025	VLDB	4.9605343e-05
11,326	Time Series Motif Discovery: A Comprehensive Evaluation	2025	VLDB	4.9605343e-05
11,478	EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection	2025	VLDB	4.9605343e-05
11,509	MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection	2025	VLDB	4.9605343e-05

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
143	LOF: Identifying Density-Based Local Outliers	2000	SIGMOD	0.00029095978
587	Efficient Algorithms for Mining Outliers from Large Data Sets	2000	SIGMOD	0.00015900873
1,636	SAND: Streaming Subsequence Anomaly Detection	2021	VLDB	9.9985086e-05
1,950	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	9.303437e-05
2,388	Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series	2020	VLDB	8.52352e-05
4,045	Mining surprising patterns using temporal description length	1998	VLDB	6.8199126e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	9,600	TAB: Unified Benchmarking of Time Series Anomaly Detection Methods	2025	VLDB
2	1,950	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB
3	8,423	Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data	2007	VLDB
4	9,522	TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection	2025	VLDB
5	11,509	MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection	2025	VLDB
6	6,090	AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data	2024	VLDB
7	5,138	TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms	2022	VLDB
8	3,734	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB
9	9,528	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB
10	6,283	An Experimental Evaluation of Anomaly Detection in Time Series	2024	VLDB