Anomaly Scoring

Anomaly Scoring is a computational method that quantifies the degree to which a data point in a time series deviates from expected behavior, distinguishing itself from other anomaly detection techniques by producing a continuous, interpretable score that reflects the likelihood or severity of anomalous behavior rather than yielding a binary classification.

69

Publications

4K

Citations

246

Authors

112

Institutions

Publications per year

2017–2026

55

Authors

246

  1. GP
    Guansong Pang

    Singapore Management University, Singapore

    4 publications · 636 citations

    4

    h-index

  2. LH
    Luc Hendriks

    Radboud University Nijmegen, Netherlands

    3 publications · 132 citations

    3

    h-index

  3. SC
    S. Caron

    Radboud University Nijmegen, Netherlands

    3 publications · 132 citations

    3

    h-index

  4. CS
    Chunhua Shen

    The University of Adelaide, Australia

    3 publications · 614 citations

    3

    h-index

  5. SO
    Sydney Otten

    Instituto de Física Corpuscular, Spain

    2 publications · 72 citations

    2

    h-index

1–5 of 246

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 410 citations

    8

    h-index

  2. IEEE Access

    4 publications · 142 citations

    5

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 487 citations

    2

    h-index

  4. The VLDB Journal

    1 publications · 16 citations

    1

    h-index

  5. The Astronomical Journal

    AJ · 1 publications · 27 citations

    1

    h-index

1–5