Bayesian Demand Forecasting

Bayesian Demand Forecasting is a probabilistic forecasting method that integrates prior demand knowledge and observed data through Bayesian inference to dynamically update demand predictions and quantify uncertainty in demand estimation.

52

Publications

2.5K

Citations

149

Authors

85

Institutions

Publications per year

2017–2026

17

Authors

149

  1. JC
    Jian Chai

    Xidian University, China

    3 publications · 60 citations

    3

    h-index

  2. VF
    Valentín Flunkert

    Technische Universität Berlin, Germany

    2 publications · 88 citations

    2

    h-index

  3. DS
    David Salinas

    Amazon (Germany), Germany

    2 publications · 88 citations

    2

    h-index

  4. MO
    Michael Obersteiner

    International Institute for Applied Systems Analysis, Austria

    2 publications · 48 citations

    2

    h-index

  5. HS
    Haiyan Song

    Hong Kong Polytechnic University, Hong Kong SAR China

    2 publications · 138 citations

    2

    h-index

1–5 of 149

Venues

  1. Management Science

    4 publications · 553 citations

    4

    h-index

  2. International Journal of Forecasting

    2 publications · 91 citations

    2

    h-index

  3. Energies

    2 publications · 48 citations

    2

    h-index

  4. Operations Research

    2 publications · 237 citations

    2

    h-index

  5. Annals of Tourism Research

    1 publications · 235 citations

    1

    h-index

1–5