Forecast Uncertainty Quantification

Forecast Uncertainty Quantification is a formal methodological framework within predictive analytics that characterizes the inherent stochastic variability and systematic lack of precision in forecasted outcomes by quantifying the range and likelihood of potential deviations between predicted and actual values under uncertain conditions.

493

Publications

29.8K

Citations

1.6K

Authors

664

Institutions

Publications per year

2017–2026

189

Variants

Forecast Uq

Authors

1.6K

  1. DR
    David Robertson

    CSIRO Land and Water, Australia

    9 publications · 683 citations

    9

    h-index

  2. RG
    Rangan Gupta

    University of Pretoria, South Africa

    7 publications · 700 citations

    7

    h-index

  3. XS
    Xuguang Simon Sheng

    American University, United States

    7 publications · 366 citations

    7

    h-index

  4. QJ
    Quan J. Wang

    CSIRO Land and Water, Australia

    7 publications · 622 citations

    7

    h-index

  5. JT
    Joseph Tracy

    National Bureau of Economic Research, United States

    6 publications · 422 citations

    6

    h-index

1–5 of 1.6K

Venues

  1. Monthly Weather Review

    21 publications · 1.8K citations

    18

    h-index

  2. SSRN Electronic Journal

    Repository · 25 publications · 879 citations

    16

    h-index

  3. Weather and Forecasting

    17 publications · 1.1K citations

    16

    h-index

  4. 14

    h-index

  5. Hydrology and earth system sciences

    13 publications · 860 citations

    13

    h-index

1–5