Anomaly Detection Benchmarking

Anomaly Detection Benchmarking is a systematic evaluation framework that standardizes the comparative assessment of anomaly detection methods in time series by defining consistent datasets, performance metrics, and experimental protocols to enable objective validation and reproducibility across studies.

104

Publications

9.9K

Citations

462

Authors

180

Institutions

Publications per year

2017–2026

83

Authors

462

  1. TG
    Thomas G. Dietterich

    Oregon State University, United States

    4 publications · 209 citations

    4

    h-index

  2. TP
    Themis Palpanas

    Université Paris Cité, France

    4 publications · 206 citations

    4

    h-index

  3. JP
    John Paparrizos

    University of Chicago, United States

    4 publications · 206 citations

    4

    h-index

  4. PB
    Paul Boniol

    Université Paris Cité, France

    4 publications · 206 citations

    4

    h-index

  5. RA
    Roy A. Maxion

    Carnegie Mellon University, United States

    3 publications · 703 citations

    3

    h-index

1–5 of 462

Venues

  1. arXiv (Cornell University)

    Repository · 19 publications · 1K citations

    14

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 9 publications · 382 citations

    9

    h-index

  3. Computers & Security

    2 publications · 1.4K citations

    2

    h-index

  4. IEEE Access

    2 publications · 604 citations

    2

    h-index

  5. Applied Sciences

    2 publications · 98 citations

    2

    h-index

1–5