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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
h9fbb9c1c34e96a0a
Venue
VLDB
Year
2025
Pagerank
5.1708619e-05
Overall Rank
9,482 | 36.25%
DOI
10.14778/3749646.3749699

Incoming Non-self Citations Over Time

Authors

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 7 of 7 citing papers.

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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 0.00029202746
583 Efficient Algorithms for Mining Outliers from Large Data Sets 2000 SIGMOD 0.00015960125
1,005 Anomaly Detection in Time Series: A Comprehensive Evaluation 2022 VLDB 0.00012590067
1,579 k-Shape: Efficient and Accurate Clustering of Time Series 2015 SIGMOD 0.00010186397
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
1,973 Decomposed Bounded Floats for Fast Compression and Queries 2021 VLDB 9.2834606e-05
2,375 Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series 2020 VLDB 8.555841e-05
3,298 Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection 2022 VLDB 7.4447462e-05
4,103 AutoOD: Automatic Outlier Detection 2023 SIGMOD 6.8066074e-05
4,316 GRAIL: Efficient Time-Series Representation Learning 2019 VLDB 6.6683448e-05
4,703 Debunking Four Long-Standing Misconceptions of Time-Series Distance Measures 2020 SIGMOD 6.4621968e-05
5,471 Good to the Last Bit: Data-Driven Encoding with CodecDB 2021 SIGMOD 6.1181669e-05
6,073 AutoTSAD: Unsupervised Holistic Anomaly Detection for Time Series Data 2024 VLDB 5.8953069e-05
6,189 VergeDB: A Database for IoT Analytics on Edge Devices 2021 CIDR 5.8568233e-05
7,094 Time-Series Clustering: A Comprehensive Study of Data Mining, Machine Learning, and Deep Learning Methods 2025 VLDB 5.601767e-05
7,641 A Structured Study of Multivariate Time-Series Distance Measures 2025 SIGMOD 5.4772833e-05
8,160 PIDS: Attribute Decomposition for Improved Compression and Query Performance in Columnar Storage 2020 VLDB 5.3871591e-05
9,626 Theseus: Navigating the Labyrinth of Time-Series Anomaly Detection 2022 VLDB 5.1483755e-05
9,658 Odyssey: An Engine Enabling The Time-Series Clustering Journey 2023 VLDB 5.1453267e-05
11,419 EasyAD: A Demonstration of Automated Solutions for Time-Series Anomaly Detection 2025 VLDB 4.9793485e-05
11,749 Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances 2023 VLDB 4.9793485e-05
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