Gaussian Process Forecasting

Gaussian Process Forecasting is a probabilistic forecasting method that uses Gaussian processes as priors over functions to model time series data, distinguishing itself by providing full posterior distributions over future values through non-parametric Bayesian inference.

108

Publications

7.9K

Citations

283

Authors

147

Institutions

Publications per year

2017–2026

65

Variants

Gp Forecasting

Authors

283

  1. XX
    Xiaojie Xu

    North Carolina State University, United States

    13 publications · 1.7K citations

    13

    h-index

  2. BJ
    Bingzi Jin

    North Carolina State University, United States

    11 publications · 1.5K citations

    11

    h-index

  3. AG
    Agathe Girard

    University of Glasgow, United Kingdom

    4 publications · 588 citations

    4

    h-index

  4. DC
    Di Cao

    University of Electronic Science and Technology of China, China

    3 publications · 119 citations

    3

    h-index

  5. WH
    Weihao Hu

    Aalborg University, Denmark

    3 publications · 119 citations

    3

    h-index

1–5 of 283

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 168 citations

    4

    h-index

  2. Applied Energy

    3 publications · 408 citations

    3

    h-index

  3. IEEE Transactions on Power Systems

    3 publications · 555 citations

    3

    h-index

  4. IFAC-PapersOnLine

    3 publications · 76 citations

    3

    h-index

  5. 3

    h-index

1–5