Point Anomaly Detection

Point Anomaly Detection is a type of anomaly detection task in time series analysis that identifies individual data points deviating significantly from the normal pattern of the surrounding data, distinguished by the isolation of anomalous instances at the level of single observations rather than sequences or patterns.

44

Publications

1.5K

Citations

144

Authors

63

Institutions

Publications per year

2017–2026

34

Variants

Point-based Anomaly Detection

Authors

144

  1. LX
    Liang Xiong

    Carnegie Mellon University, United States

    3 publications · 176 citations

    3

    h-index

  2. JS
    Jeff Schneider

    Carnegie Mellon University, United States

    3 publications · 176 citations

    3

    h-index

  3. BP
    Barnabás Póczos

    Carnegie Mellon University, United States

    3 publications · 176 citations

    3

    h-index

  4. AT
    Alexander T. M. Fisch

    Lancaster University, United Kingdom

    3 publications · 52 citations

    3

    h-index

  5. PF
    Paul Fearnhead

    Lancaster University, United Kingdom

    3 publications · 52 citations

    3

    h-index

1–5 of 144

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 79 citations

    4

    h-index

  2. IEEE Access

    1 publications · 37 citations

    2

    h-index

  3. Figshare

    Repository · 2 publications · 99 citations

    2

    h-index

  4. 1

    h-index

  5. 1

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1–5