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: 0.00012584107
Overall Rank: 1,005 | 93.25%
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
3,728	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB	7.0667899e-05
4,910	METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection	2024	VLDB	6.3554126e-05
5,118	TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms	2022	VLDB	6.264978e-05
5,561	ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection	2024	VLDB	6.0820916e-05
6,075	AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data	2024	VLDB	5.8925161e-05
6,272	An Experimental Evaluation of Anomaly Detection in Time Series	2024	VLDB	5.8268397e-05
7,101	Time Series Data Mining: A Unifying View	2023	VLDB	5.5991152e-05
7,252	Akane: Perplexity-Guided Time Series Data Cleaning	2024	SIGMOD	5.5714652e-05
7,577	Benchmarking the Utility of w-event Differential Privacy Mechanisms - When Baselines Become Mighty Competitors	2023	VLDB	5.4914399e-05
7,646	KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection	2025	SIGMOD	5.4746904e-05
8,031	TSM-Bench: Benchmarking Time Series Database Systems for Monitoring Applications	2023	VLDB	5.4019638e-05
9,086	A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis	2024	VLDB	5.2258409e-05
9,493	TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection	2025	VLDB	5.168414e-05
9,499	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB	5.168414e-05
9,572	Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous Domains	2025	VLDB	5.154741e-05
9,575	TAB: Unified Benchmarking of Time Series Anomaly Detection Methods	2025	VLDB	5.154741e-05
9,879	Clean4TSDB: A Data Cleaning Tool for Time Series Databases	2024	VLDB	5.115241e-05
10,480	HYDRA: A Multi-Level Hierarchy-Driven Approach for Robust Anomaly Detection in Time Series	2026	SIGMOD	4.9769913e-05
10,521	The Power of Anomaly Detection in Predictive Maintenance: [Experiments & Analysis]	2026	SIGMOD	4.9769913e-05
10,565	Cleaning Time Series under Seasonal and Trend Constraints	2026	SIGMOD	4.9769913e-05
10,770	KDSelector: A Framework of Knowledge-Enhanced and Data-Efficient Selector Learning for Anomaly Detection Model Selection in Time Series	2026	VLDB	4.9769913e-05
11,257	Streaming Time Series Subsequence Anomaly Detection: A Glance and Focus Approach	2025	VLDB	4.9769913e-05
11,270	Time Series Motif Discovery: A Comprehensive Evaluation	2025	VLDB	4.9769913e-05
11,425	EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection	2025	VLDB	4.9769913e-05
11,457	MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection	2025	VLDB	4.9769913e-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	0.00029189529
583	Efficient Algorithms for Mining Outliers from Large Data Sets	2000	SIGMOD	0.00015952617
1,629	SAND: Streaming Subsequence Anomaly Detection	2021	VLDB	0.0001003165
1,938	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB	9.334286e-05
2,376	Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series	2020	VLDB	8.5517908e-05
4,028	Mining surprising patterns using temporal description length	1998	VLDB	6.8425197e-05

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#	Overall Rank	Paper	Year	Venue
1	9,575	TAB: Unified Benchmarking of Time Series Anomaly Detection Methods	2025	VLDB
2	1,938	TSB-UAD: An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection	2022	VLDB
3	8,406	Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data	2007	VLDB
4	9,493	TSB-AutoAD: Towards Automated Solutions for Time-Series Anomaly Detection	2025	VLDB
5	11,457	MLP-Mixer based Masked Autoencoders Are Effective, Explainable and Robust for Time Series Anomaly Detection	2025	VLDB
6	6,075	AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data	2024	VLDB
7	5,118	TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms	2022	VLDB
8	3,728	Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series	2023	VLDB
9	9,499	Time-Series Anomaly Detection: Overview and New Trends	2024	VLDB
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