Forecasting With Missing Data
Forecasting with Missing Data is a methodological framework in epidemic forecasting that integrates statistical or machine learning techniques to generate reliable predictions despite incomplete or partially observed epidemiological data by explicitly modeling the mechanisms and patterns of data absence.
83
Publications
3.7K
Citations
335
Authors
148
Institutions
Publications per year
2017–2026
55
Parents
83
Graph Markov network for traffic forecasting with missing data
Zhiyong Cui, Longfei Lin, Ziyuan Pu et al. · Transportation Research Part C Emerging Technologies · 2020 · 132 citations
Computing, Data Science, Forecasting +13
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148

Pennsylvania State University
State College, United States
3 h-index
13 publications
857 citations
National University of Singapore
Singapore, Singapore
3 h-index
8 publications
694 citations

University of Waterloo
Waterloo, Canada
3 h-index
5 publications
272 citations
Columbia University
New York, United States
2 h-index
4 publications
156 citations
Cardiff University
Cardiff, United Kingdom
2 h-index
1 publications
40 citations
University of California, Los Angeles
Los Angeles, United States
2 h-index
10 publications
482 citations
University of Chicago
Chicago, United States
2 h-index
4 publications
516 citations

Australian National University
Canberra, Australia
2 h-index
2 publications
111 citations
1–8 of 148
4 publications · 369 citations
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Repository · 3 publications · 90 citations
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2 publications · 105 citations
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2 publications · 34 citations
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2 publications · 46 citations
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