Probabilistic Anomaly Detection

Probabilistic Anomaly Detection is a methodological framework within anomaly detection in time series that characterizes normal behavior through probabilistic models and identifies anomalies as time points where the observed data exhibit significantly low likelihood under the estimated probability distribution.

273

Publications

16.6K

Citations

862

Authors

380

Institutions

Publications per year

2017–2026

113

Authors

862

  1. QZ
    Qing Zhao

    University of California, Davis, United States

    8 publications · 244 citations

    8

    h-index

  2. KC
    Kobi Cohen

    Ben-Gurion University of the Negev, Israel

    7 publications · 225 citations

    7

    h-index

  3. PW
    Peter Willett

    University of Connecticut, United States

    5 publications · 169 citations

    5

    h-index

  4. IC
    Ioannis Ch. Paschalidis

    Boston University, United States

    4 publications · 108 citations

    4

    h-index

  5. HG
    Huanbo Gao

    Dalian University of Technology, China

    3 publications · 226 citations

    3

    h-index

1–5 of 862

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 277 citations

    9

    h-index

  2. IEEE Transactions on Signal Processing

    7 publications · 260 citations

    7

    h-index

  3. Information Sciences

    4 publications · 149 citations

    4

    h-index

  4. Expert Systems with Applications

    4 publications · 169 citations

    4

    h-index

  5. Mechanical Systems and Signal Processing

    MSSP · 4 publications · 151 citations

    4

    h-index

1–5