Forecasting With Exogenous Variables

Forecasting with Exogenous Variables is a time series forecasting approach that incorporates external predictor variables, distinct from the target series, to improve prediction accuracy by modeling their influence on the observed temporal dynamics.

279

Publications

13K

Citations

828

Authors

448

Institutions

Publications per year

2017–2026

145

Variants

Exogenous Time Series Forecasting

Authors

828

  1. TG
    Tian Guo

    École Polytechnique Fédérale de Lausanne, Switzerland

    4 publications · 168 citations

    4

    h-index

  2. HT
    Hugo T.C. Pedro

    University of California San Diego, United States

    3 publications · 821 citations

    3

    h-index

  3. AA
    Afshin Afshari

    Fraunhofer Institute for Building Physics, Germany

    3 publications · 484 citations

    3

    h-index

  4. LF
    Laurent Ferrara

    Banque de France, France

    3 publications · 90 citations

    3

    h-index

  5. CF
    Carlos F.M. Coimbra

    University of California San Diego, United States

    3 publications · 821 citations

    3

    h-index

1–5 of 828

Venues

  1. Energies

    15 publications · 839 citations

    15

    h-index

  2. arXiv (Cornell University)

    Repository · 7 publications · 295 citations

    7

    h-index

  3. IEEE Transactions on Power Systems

    6 publications · 994 citations

    6

    h-index

  4. International Journal of Forecasting

    5 publications · 536 citations

    5

    h-index

  5. Journal of Forecasting

    5 publications · 211 citations

    5

    h-index

1–5