Data-driven Demand Forecasting

Data-Driven Demand Forecasting is a demand forecasting approach that belongs to the class of forecasting methodologies and is distinguished by its reliance on empirical data and statistical or machine learning models—rather than theoretical assumptions or qualitative judgment—to identify patterns, trends, and relationships for predicting future demand.

240

Publications

12.6K

Citations

803

Authors

391

Institutions

Publications per year

2017–2026

156

Authors

803

  1. SA
    Saud Al-Fattah

    Saudi Aramco (United States), United States

    3 publications · 66 citations

    3

    h-index

  2. YB
    Yung-Cheol Byun

    Jeju National University, South Korea

    3 publications · 79 citations

    3

    h-index

  3. LC
    Li-Yueh Chen

    MingDao University, Taiwan

    2 publications · 37 citations

    2

    h-index

  4. AK
    Andrea Kolková

    VSB - Technical University of Ostrava, Czechia

    2 publications · 40 citations

    2

    h-index

  5. JY
    Jie Yang

    Northeastern University, China

    2 publications · 57 citations

    2

    h-index

1–5 of 803

Venues

  1. Expert Systems with Applications

    7 publications · 210 citations

    7

    h-index

  2. 6

    h-index

  3. Energy

    5 publications · 598 citations

    5

    h-index

  4. Applied Energy

    5 publications · 587 citations

    5

    h-index

  5. IEEE Access

    5 publications · 209 citations

    5

    h-index

1–5