Deep Learning-based Forecasting
Deep Learning-Based Forecasting is a traffic demand forecasting method that leverages deep neural network architectures to automatically learn complex spatiotemporal patterns from large-scale transportation data, distinguishing it from traditional statistical or shallow machine learning approaches.
3.6K
Publications
267.6K
Citations
12K
Authors
2.8K
Institutions
Publications per year
2017–2026
3.5K
Parents
3.6K
Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
Weicong Kong, Zhao Yang Dong, Youwei Jia et al. · IEEE Transactions on Smart Grid · 2017 · 2.5K citations
Computing, Data Science, Forecasting +16
1–5 of 3.6K
12K
12
h-index
11
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11
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Beijing University of Posts and Telecommunications, China
10 publications · 1.3K citations
10
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10
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1–5 of 12K
2.8K

Chinese Academy of Sciences
Beijing, China
47 h-index
230 publications
33.7K citations
Tsinghua University
Beijing, China
35 h-index
151 publications
19.7K citations
Beijing Jiaotong University
Beijing, China
35 h-index
212 publications
36K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
31 h-index
76 publications
8.1K citations

Zhejiang University
Hangzhou, China
28 h-index
92 publications
9.4K citations
University of Chinese Academy of Sciences
Beijing, China
28 h-index
93 publications
8.1K citations

Southeast University
Nanjing, China
27 h-index
117 publications
6.4K citations

Beihang University
Beijing, China
25 h-index
116 publications
17.4K citations
1–8 of 2.8K
189 publications · 15.1K citations
69
h-index
75 publications · 10.2K citations
45
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Repository · 135 publications · 8.5K citations
44
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75 publications · 5.4K citations
38
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60 publications · 6.8K citations
37
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1–5