Multivariate Change-point Detection

Multivariate Change-Point Detection is a change-point detection framework that identifies abrupt alterations in the joint distribution of multivariate time series data, distinguished from univariate approaches by simultaneously accounting for dependencies and structural changes across multiple correlated variables.

118

Publications

5.2K

Citations

277

Authors

192

Institutions

Publications per year

2017–2026

42

Authors

277

  1. DM
    Douglas M. Hawkins

    University of Minnesota, United States

    4 publications · 327 citations

    4

    h-index

  2. CK
    Claudia Kirch

    Otto-von-Guericke-Universität Magdeburg, Germany

    3 publications · 126 citations

    3

    h-index

  3. PK
    Peter Kuppens

    KU Leuven, Belgium

    3 publications · 138 citations

    3

    h-index

  4. JC
    Jedelyn Cabrieto

    KU Leuven, Belgium

    3 publications · 138 citations

    3

    h-index

  5. LH
    Lajos Horváth

    University of Utah, United States

    3 publications · 113 citations

    3

    h-index

1–5 of 277

Venues

  1. arXiv (Cornell University)

    Repository · 7 publications · 144 citations

    7

    h-index

  2. 5

    h-index

  3. 4

    h-index

  4. Technometrics

    4 publications · 541 citations

    4

    h-index

  5. Journal of Quality Technology

    3 publications · 106 citations

    3

    h-index

1–5