Probabilistic Hierarchical Forecasting is a hierarchical forecasting framework that models uncertainty through probabilistic distributions across multiple levels of aggregation, enabling coherent and calibrated predictions while preserving the hierarchical structure of the data.
12
Publications
641
Citations
38
Authors
22
Institutions
Publications per year
2017–2026
11
Parents
Publications
12
1–5 of 12
Authors
38
- RJ
2
h-index
- SB
2
h-index
- JW
2
h-index
- NT
1
h-index
- TJ
1
h-index
1–5 of 38
Institutions
22
University of Oxford
Oxford, United Kingdom
2 h-index
2 publications
185 citations
Colorado State University
Fort Collins, United States
1 h-index
2 publications
22 citations
Amazon (United States)
Seattle, United States
1 h-index
5 publications
75 citations
Southeast University
Nanjing, China
1 h-index
4 publications
148 citations
California Institute of Technology
Pasadena, United States
1 h-index
1 publications
29 citations
Agency for Science, Technology and Research
Singapore, Singapore
1 h-index
1 publications
29 citations
Xi'an Jiaotong University
Xi'an, China
1 h-index
3 publications
114 citations
National Park Service
Washington, United States
1 h-index
2 publications
22 citations
1–8 of 22
Venues
- International Journal of Forecasting
3 publications · 211 citations
3
h-index
- Ecosphere
1 publications · 11 citations
1
h-index
- Journal of the American Statistical Association
1 publications · 127 citations
1
h-index
- Mechanical Systems and Signal Processing
MSSP · 1 publications · 87 citations
1
h-index
- Information Sciences
1 publications · 37 citations
1
h-index
1–5