Global Forecasting Models is a class of statistical and machine learning frameworks designed to simultaneously predict future values across multiple time series by leveraging shared patterns and dependencies at a global scale, distinguishing it from traditional univariate or isolated multivariate forecasting approaches through its capacity to pool information across heterogeneous domains, regions, or entities within a unified modeling architecture.
160
Publications
9.7K
Citations
605
Authors
241
Institutions
Publications per year
2017–2026
75
Parents
Publications
160
The Met Office Global Coupled model 2.0 (GC2) configuration
K. D. Williams, Chris Harris, Alejandro Bodas‐Salcedo et al. · Geoscientific model development · 2015 · 335 citations · Full text
1–5 of 160
Authors
605
- MH
5
h-index
- IC
5
h-index
- OK
5
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- CF
5
h-index
- DL
5
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1–5 of 605
Institutions
241
Met Office
Exeter, United Kingdom
10 h-index
86 publications
12.7K citations
European Centre for Medium-Range Weather Forecasts
Reading, United Kingdom
9 h-index
35 publications
2.6K citations
National Oceanic and Atmospheric Administration
Washington, United States
7 h-index
14 publications
542 citations
Monash University
Melbourne, Australia
6 h-index
18 publications
2.2K citations
University of Reading
Reading, United Kingdom
6 h-index
7 publications
603 citations
Météo-France
Toulouse, France
6 h-index
7 publications
251 citations
University of Southern California
Los Angeles, United States
5 h-index
5 publications
1.8K citations
Bureau of Meteorology
Melbourne, Australia
5 h-index
8 publications
428 citations
1–8 of 241
Venues
- Monthly Weather Review
11 publications · 388 citations
11
h-index
- Weather and Forecasting
7 publications · 422 citations
7
h-index
- SSRN Electronic Journal
Repository · 7 publications · 214 citations
7
h-index
- Quarterly Journal of the Royal Meteorological Society
6 publications · 312 citations
6
h-index
- Geoscientific model development
5 publications · 658 citations
5
h-index
1–5