Outlier Features

Outlier Features is a type of time series feature that captures anomalous data points or extreme deviations from the expected pattern within a temporal sequence, distinguished by its specific focus on identifying and quantifying irregular observations that may indicate noise, measurement error, or significant events.

130

Publications

7.1K

Citations

467

Authors

238

Institutions

Publications per year

2017–2026

67

Authors

467

  1. XH
    Xia Hu

    Texas A&M University, United States

    3 publications · 84 citations

    3

    h-index

  2. JC
    Jerry Chun‐Wei Lin

    Western Norway University of Applied Sciences, Norway

    2 publications · 101 citations

    2

    h-index

  3. DK
    David Kaeli

    Northeastern University, United States

    2 publications · 47 citations

    2

    h-index

  4. RJ
    Rob J. Hyndman

    Monash University, Australia

    2 publications · 230 citations

    2

    h-index

  5. TS
    Thomas S. Huang

    University of Illinois Urbana-Champaign, United States

    2 publications · 24 citations

    2

    h-index

1–5 of 467

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 96 citations

    5

    h-index

  2. Quarterly Journal of the Geological Society

    3 publications · 74 citations

    3

    h-index

  3. IEEE Transactions on Image Processing

    2 publications · 95 citations

    2

    h-index

  4. 2

    h-index

  5. Applied Sciences

    2 publications · 68 citations

    2

    h-index

1–5