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An Experimental Evaluation of Anomaly Detection in Time Series

Summary: Systematic benchmark of 17 time-series anomaly detectors across a taxonomy of data dimensions, techniques, and anomaly types. Evaluates effectiveness, efficiency, and robustness on real/synthetic data using point and subsequence-aware range metrics, yielding practical method-selection guidance. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13913
Venue
VLDB
Year
2024
Pagerank
5.9628456e-05
Overall Rank
6,141 | 57.87%
DOI
10.14778/3632093.3632110

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Authors

BibTeX Citation

@article{zhang_vldb24,
        title = {{An Experimental Evaluation of Anomaly Detection in Time Series}},
        author = {Zhang, Aoqian and Deng, Shuqing and Cui, Dongping and Yuan, Ye and Wang, Guoren},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {3},
        pages = {483--496},
        doi = {10.14778/3632093.3632110},
        url = {https://doi.org/10.14778/3632093.3632110},
        year = {2024}
}

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