Regression Forecasting Models

Regression Forecasting Models is a statistical modeling approach that belongs to the class of quantitative forecasting methods, distinguished by its use of regression analysis to estimate the relationships between dependent workforce demand variables and one or more independent predictor variables for the purpose of projecting future labor requirements.

199

Publications

8.4K

Citations

501

Authors

271

Institutions

Publications per year

2017–2026

56

Authors

501

  1. EK
    E. Koenig

    Institute of Nuclear Physics of Lyon, France

    3 publications · 345 citations

    3

    h-index

  2. JM
    James M.W. Wong

    Hong Kong Polytechnic University, Hong Kong SAR China

    3 publications · 80 citations

    3

    h-index

  3. JM
    Jaime Miranda

    University of Chile, Chile

    2 publications · 35 citations

    2

    h-index

  4. PP
    Ping‐Feng Pai

    National Chi Nan University, Taiwan

    2 publications · 67 citations

    2

    h-index

  5. CF
    Cèsar Ferri

    Universitat Politècnica de València, Spain

    1 publications · 41 citations

    2

    h-index

1–5 of 501

Venues

  1. Journal of Forecasting

    7 publications · 177 citations

    7

    h-index

  2. SSRN Electronic Journal

    Repository · 6 publications · 321 citations

    6

    h-index

  3. International Journal of Forecasting

    5 publications · 396 citations

    5

    h-index

  4. 5

    h-index

  5. Procedia Computer Science

    4 publications · 140 citations

    4

    h-index

1–5