Data-driven Forecasting Models

Data-Driven Forecasting Models is a forecasting methodology that belongs to the class of predictive models and is distinguished by its reliance on empirical data and statistical learning techniques to identify patterns and generate predictions without requiring explicit mechanistic or theoretical assumptions about the underlying system.

318

Publications

17.9K

Citations

1.2K

Authors

539

Institutions

Publications per year

2017–2026

260

Authors

1.2K

  1. ÖK
    Özgür Kişi

    Ilia State University, Georgia

    4 publications · 194 citations

    4

    h-index

  2. MZ
    Mohammad Zounemat‐Kermani

    Shahid Bahonar University of Kerman, Iran

    4 publications · 178 citations

    4

    h-index

  3. NA
    Nadhir Al‐Ansari

    Luleå University of Technology, Sweden

    4 publications · 117 citations

    4

    h-index

  4. AE
    Ahmed Elbeltagi

    Mansoura University, Egypt

    4 publications · 195 citations

    4

    h-index

  5. GC
    Gerald Corzo

    IHE Delft Institute for Water Education, Netherlands

    3 publications · 226 citations

    3

    h-index

1–5 of 1.2K

Venues

  1. Water

    20 publications · 899 citations

    17

    h-index

  2. arXiv (Cornell University)

    Repository · 9 publications · 226 citations

    9

    h-index

  3. Hydrological Sciences Journal

    8 publications · 566 citations

    8

    h-index

  4. Hydrology and earth system sciences

    8 publications · 785 citations

    8

    h-index

  5. IEEE Access

    7 publications · 275 citations

    7

    h-index

1–5