Hierarchical Forecasting

Hierarchical Forecasting is a forecasting methodology that organizes multiple time series into a hierarchy based on aggregation structures, distinguishing itself by simultaneously producing forecasts across all levels of the hierarchy and ensuring coherency between them through reconciliation techniques.

207

Publications

10.9K

Citations

567

Authors

286

Institutions

Publications per year

2017–2026

119

Authors

567

  1. RJ
    Rob J. Hyndman

    Monash University, Australia

    14 publications · 1.8K citations

    12

    h-index

  2. GA
    George Athanasopoulos

    Monash University, Australia

    8 publications · 1.4K citations

    8

    h-index

  3. DZ
    Dehua Zheng

    Goldwind (China), China

    5 publications · 159 citations

    5

    h-index

  4. AT
    Abinet Tesfaye Eseye

    North China Electric Power University, China

    5 publications · 159 citations

    5

    h-index

  5. SB
    Souhaib Ben Taieb

    Monash University, Australia

    4 publications · 259 citations

    4

    h-index

1–5 of 567

Venues

  1. International Journal of Forecasting

    14 publications · 870 citations

    12

    h-index

  2. Applied Energy

    9 publications · 616 citations

    8

    h-index

  3. arXiv (Cornell University)

    Repository · 7 publications · 250 citations

    7

    h-index

  4. Expert Systems with Applications

    7 publications · 349 citations

    6

    h-index

  5. Information Sciences

    5 publications · 512 citations

    5

    h-index

1–5