Statistical Summary Features

Statistical Summary Features is a type of time series feature extraction method that characterizes the distributional properties of a time series through aggregate statistical measures such as mean, variance, skewness, and kurtosis, distinguishing it from other methods by summarizing the overall temporal behavior in a reduced-dimensional numerical form.

109

Publications

8.4K

Citations

316

Authors

144

Institutions

Publications per year

2017–2026

38

Authors

316

  1. DW
    David Whitney

    University of California, Berkeley, United States

    7 publications · 965 citations

    7

    h-index

  2. JH
    Jason Haberman

    University of California, Davis, United States

    4 publications · 669 citations

    4

    h-index

  3. KJ
    Kenneth J. Powell

    University of Exeter, United Kingdom

    2 publications · 67 citations

    2

    h-index

  4. JL
    Jerry L. Hintze

    Brigham Young University, United States

    2 publications · 2K citations

    2

    h-index

  5. TS
    Theofanis Sapatinas

    University of Cyprus, Cyprus

    2 publications · 67 citations

    2

    h-index

1–5 of 316

Venues

  1. Journal of Vision

    7 publications · 391 citations

    7

    h-index

  2. Psychological Science

    5 publications · 709 citations

    5

    h-index

  3. 4

    h-index

  4. PubMed

    Repository · 3 publications · 100 citations

    3

    h-index

  5. 3

    h-index

1–5