Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Networks
Author:
Affiliation:
1. HKUST(GZ), Guangzhou, China
2. Baidu Research, Beijing, China
3. HKUST, Hong Kong, China
4. HKUST(GZ) & HKUST, Guangzhou, China
5. Arizona State University, Phoenix, USA
6. Baidu Inc., Beijing, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3671665
Reference54 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. Shaojie Bai, J Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018).
3. Patient Subtyping via Time-Aware LSTM Networks
4. Z Che, S Purushotham, K Cho, D Sontag, and Y Liu. 2018. Recurrent Neural Networks for Multivariate Time Series with Missing Values. Scientific reports (2018), 6085--6085.
5. Adaptive Ramp Metering Control for Urban Freeway Using Large-Scale Data
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