Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series
Summary: Evaluate time-series classification for anomaly-detector selection: 17 classifiers on 1,800 series, showing classifier-based selection outperforms all individual detectors with similar runtime. First large-scale baseline proving TS classification enables accurate, efficient model selection for AutoML anomaly pipelines. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Emmanouil Sylligardos (Hellas)
- 2. Paul Boniol (Université Paris Cité)
- 3. John Paparrizos (Ohio State University)
- 4. Panos Trahanias (Hellas)
- 5. Themis Palpanas (Institut Universitaire de France; Université Paris Cité)
BibTeX Citation
@article{sylligardos_vldb23,
title = {{Choose Wisely: An Extensive Evaluation of Model Selection for Anomaly Detection in Time Series}},
author = {Sylligardos, Emmanouil and Boniol, Paul and Paparrizos, John and Trahanias, Panos and Palpanas, Themis},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {11},
pages = {3418--3432},
doi = {10.14778/3611479.3611536},
url = {https://doi.org/10.14778/3611479.3611536},
year = {2023}
}
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