Forecasting Under Data Sparsity
Forecasting Under Data Sparsity is a predictive modeling framework in the domain of traffic participant behavior prediction that specifically addresses the challenge of generating accurate future trajectory estimates when limited observational data is available for training or inference.
36
Publications
1.4K
Citations
88
Authors
39
Institutions
Publications per year
2017–2026
28
36
TimeLLM: Time Series Forecasting by Reprogramming Large Language Models
Ming Jin, James Y. Zhang · 2026 · 131 citations · Full text
1–5 of 36
88
2
h-index
2
h-index
2
h-index
2
h-index
1
h-index
1–5 of 88
39
National University of Singapore
Singapore, Singapore
3 h-index
4 publications
406 citations

University of Strathclyde
Glasgow, United Kingdom
2 h-index
2 publications
101 citations
Nanyang Technological University
Singapore, Singapore
2 h-index
3 publications
141 citations
European Central Bank
Frankfurt am Main, Germany
2 h-index
2 publications
101 citations
Zhejiang University of Science and Technology
Hangzhou, China
2 h-index
2 publications
26 citations

Zhejiang University
Hangzhou, China
2 h-index
3 publications
211 citations
University of Southampton
Southampton, United Kingdom
2 h-index
3 publications
129 citations
Singapore-MIT Alliance for Research and Technology
Singapore, Singapore
2 h-index
2 publications
65 citations
1–8 of 39
4 publications · 322 citations
4
h-index
2 publications · 78 citations
2
h-index
1 publications · 23 citations
1
h-index
Repository · 1 publications · 31 citations
1
h-index
1 publications · 13 citations
1
h-index
1–5