Count Data Forecasting

Count Data Forecasting is a predictive modeling methodology that specifically addresses the estimation of future count-valued outcomes—non-negative integers arising from discrete events—by leveraging statistical or machine learning models tailored to the distributional properties of count data, such as overdispersion, excess zeros, and non-negative support.

30

Publications

1.1K

Citations

83

Authors

65

Institutions

Publications per year

2017–2026

14

Authors

83

  1. RG
    Rainer Göb

    University of Würzburg, Germany

    2 publications · 34 citations

    2

    h-index

  2. RM
    Raju Maiti

    Indian Statistical Institute, India

    2 publications · 31 citations

    2

    h-index

  3. CW
    Christian Weiß

    Helmut Schmidt University, Germany

    2 publications · 34 citations

    2

    h-index

  4. LC
    Layth C. Alwan

    University of Wisconsin–Milwaukee, United States

    2 publications · 34 citations

    2

    h-index

  5. AH
    Annika Homburg

    Helmut Schmidt University, Germany

    2 publications · 34 citations

    2

    h-index

1–5 of 83

Venues

  1. Journal of Forecasting

    3 publications · 39 citations

    3

    h-index

  2. International Journal of Forecasting

    2 publications · 263 citations

    2

    h-index

  3. PLoS ONE

    2 publications · 56 citations

    2

    h-index

  4. 2

    h-index

  5. AStA Advances in Statistical Analysis

    1 publications · 113 citations

    1

    h-index

1–5