High-dimensional Forecasting

High-Dimensional Forecasting is a forecasting methodology characterized by the prediction of multivariate time series in which the number of variables approaches or exceeds the number of observational time points, necessitating specialized statistical or computational techniques to address issues of dimensionality, collinearity, and overfitting.

99

Publications

5.9K

Citations

306

Authors

184

Institutions

Publications per year

2017–2026

65

Authors

306

  1. KA
    Kazuyuki Aihara

    The University of Tokyo, Japan

    3 publications · 174 citations

    3

    h-index

  2. HM
    Huanfei Ma

    Soochow University, China

    3 publications · 174 citations

    3

    h-index

  3. LC
    Luonan Chen

    Chinese Academy of Sciences, China

    3 publications · 174 citations

    3

    h-index

  4. AB
    Ali Ben Abbes

    Manouba University, Tunisia

    2 publications · 56 citations

    2

    h-index

  5. MB
    Michael Bohlke‐Schneider

    Technische Universität Berlin, Germany

    2 publications · 105 citations

    2

    h-index

1–5 of 306

Venues

  1. International Journal of Forecasting

    7 publications · 523 citations

    7

    h-index

  2. Expert Systems with Applications

    7 publications · 589 citations

    7

    h-index

  3. arXiv (Cornell University)

    Repository · 5 publications · 400 citations

    5

    h-index

  4. Neurocomputing

    3 publications · 188 citations

    3

    h-index

  5. Journal of Econometrics

    3 publications · 424 citations

    3

    h-index

1–5