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: 0.00012590067
Overall Rank: 1,005 | 93.25%
DOI: 10.14778/3538598.3538602

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
3,726	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB	7.0701368e-05
4,909	METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection	2024	VLDB	6.3584226e-05
5,115	TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms	2022	VLDB	6.2679451e-05
5,559	ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection	2024	VLDB	6.0849722e-05
6,073	AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data	2024	VLDB	5.8953069e-05
6,268	An Experimental Evaluation of Anomaly Detection in Time Series	2024	VLDB	5.8295994e-05
7,099	Time Series Data Mining: A Unifying View	2023	VLDB	5.601767e-05
7,250	Akane: Perplexity-Guided Time Series Data Cleaning	2024	SIGMOD	5.574104e-05
7,571	Benchmarking the Utility of w-event Differential Privacy Mechanisms - When Baselines Become Mighty Competitors	2023	VLDB	5.4940407e-05
7,640	KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection	2025	SIGMOD	5.4772833e-05
8,026	TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications	2023	VLDB	5.4045222e-05
9,077	A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis	2024	VLDB	5.2283159e-05
9,482	TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection	2025	VLDB	5.1708619e-05
9,488	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB	5.1708619e-05
9,564	Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains	2025	VLDB	5.1571823e-05
9,567	TAB: Unified Benchmarking of Time Series Anomaly Detection Methods	2025	VLDB	5.1571823e-05
9,872	Clean4TSDB: A Data Cleaning Tool for Time Series Databases	2024	VLDB	5.1176637e-05
10,469	HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series	2026	SIGMOD	4.9793485e-05
10,510	The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis]	2026	SIGMOD	4.9793485e-05
10,554	Cleaning Time Series under Seasonal and Trend Constraints	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
11,249	Streaming Time Series Subsequence Anomaly Detection: A Glance and Focus Approach	2025	VLDB	4.9793485e-05
11,262	Time Series Motif Discovery: A Comprehensive Evaluation	2025	VLDB	4.9793485e-05
11,419	EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection	2025	VLDB	4.9793485e-05
11,451	MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection	2025	VLDB	4.9793485e-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
142	LOF: Identifying Density-Based Local Outliers	2000	SIGMOD	0.00029202746
583	Efficient Algorithms for Mining Outliers from Large Data Sets	2000	SIGMOD	0.00015960125
1,629	SAND: Streaming Subsequence Anomaly Detection	2021	VLDB	0.00010036401
1,937	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	9.3387043e-05
2,375	Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series	2020	VLDB	8.555841e-05
4,026	Mining surprising patterns using temporal description length	1998	VLDB	6.8457565e-05

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#	Overall Rank	Paper	Year	Venue
1	9,567	TAB: Unified Benchmarking of Time Series Anomaly Detection Methods	2025	VLDB
2	1,937	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB
3	8,401	Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data	2007	VLDB
4	9,482	TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection	2025	VLDB
5	11,451	MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection	2025	VLDB
6	6,073	AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data	2024	VLDB
7	5,115	TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms	2022	VLDB
8	3,726	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB
9	9,488	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB
10	6,268	An Experimental Evaluation of Anomaly Detection in Time Series	2024	VLDB