Data-driven Forecasting Models
Data-Driven Forecasting Models is a forecasting methodology that belongs to the class of predictive models and is distinguished by its reliance on empirical data and statistical learning techniques to identify patterns and generate predictions without requiring explicit mechanistic or theoretical assumptions about the underlying system.
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Publications
17.9K
Citations
1.2K
Authors
539
Institutions
Publications per year
2017–2026
260
Parents
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Chinese Academy of Sciences
Beijing, China
7 h-index
18 publications
1.1K citations
University of Maryland, College Park
College Park, United States
7 h-index
18 publications
1.1K citations
Texas A&M University
College Station, United States
6 h-index
7 publications
323 citations
Tsinghua University
Beijing, China
6 h-index
16 publications
1.9K citations

Mansoura University
Al Mansurah, Egypt
5 h-index
5 publications
234 citations

Shahid Bahonar University of Kerman
Kerman, Iran
5 h-index
9 publications
355 citations

University of Tehran
Tehran, Iran
5 h-index
11 publications
511 citations
Luleå University of Technology
Luleå, Sweden
4 h-index
4 publications
117 citations
1–8 of 539
20 publications · 899 citations
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Repository · 9 publications · 226 citations
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8 publications · 566 citations
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8 publications · 785 citations
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7 publications · 275 citations
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