MulSTE: A Multi-view Spatio-temporal Learning Framework with Heterogeneous Event Fusion for Demand-supply Prediction
Author:
Affiliation:
1. Southeast University, Nanjing, Jiangsu, China
2. Southeast University, Nanjing, China
3. Peking University, Beijing, China
4. Rutgers University, Piscataway, USA
5. JD Logistics, Beijing, China
Funder
Natural Science Foundation of Jiangsu Province
National Science and Technology Major Project
National Natural Science Foundation of China
Jiangsu Provincial Key Research and Development Program
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3672030
Reference44 articles.
1. Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Advances in neural information processing systems, Vol. 33 (2020), 17804--17815.
2. Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014.
3. Revisiting Pre-Trained Models for Chinese Natural Language Processing
4. Historical Inertia
5. Grids Versus Graphs: Partitioning Space for Improved Taxi Demand-Supply Forecasts
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