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
Parents
108
1–5 of 108
283
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147
North Carolina State University
Raleigh, United States
12 h-index
14 publications
1.8K citations
University of Sheffield
Sheffield, United Kingdom
4 h-index
4 publications
58 citations

Meiji University
Tokyo, Japan
3 h-index
6 publications
220 citations
Max Planck Society
Munich, Germany
3 h-index
4 publications
264 citations
Max Planck Institute for Biological Cybernetics
Tübingen, Germany
3 h-index
4 publications
264 citations
Aalborg University
Aalborg, Denmark
3 h-index
6 publications
238 citations
University of Oxford
Oxford, United Kingdom
3 h-index
12 publications
804 citations

University of Electronic Science and Technology of China
Chengdu, China
3 h-index
8 publications
331 citations
1–8 of 147
Repository · 4 publications · 168 citations
4
h-index
3 publications · 408 citations
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3 publications · 555 citations
3
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3 publications · 76 citations
3
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3 publications · 191 citations
3
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1–5