A Clustering-based Multi-Task Learning Method using Graph Attention Network for Short-term Traffic Forecasting-Reference-Cited by-同舟云学术

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A Clustering-based Multi-Task Learning Method using Graph Attention Network for Short-term Traffic Forecasting

Published:2024-04-26 Issue: Volume: Page:456-463
ISSN:
Container-title:Proceedings of the 2024 10th International Conference on Computing and Artificial Intelligence
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Author:

Tan HongYuan¹^ORCID,He Pan²^ORCID,Sun Xiaoyong²^ORCID,Zhao Yongting²^ORCID

Affiliation:

1. Chongqing University of Posts and Telecommunications, China

2. Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences Chongqing School, University of Chinese Academy of Sciences, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3669754.3669825

Reference22 articles.

1. Anton Agafonov and Alexander Yumaganov. 2020. Spatio-Temporal Graph Convolutional Networks for Short-Term Traffic Forecasting. 2020 International Conference on Information Technology and Nanotechnology (ITNT) (2020), 1–6. https://api.semanticscholar.org/CorpusID:226970303

2. Christopher M Bishop. 1995. Neural networks for pattern recognition. Oxford university press.

3. George Box. 2013. Box and Jenkins: time series analysis, forecasting and control. In A Very British Affair: Six Britons and the Development of Time Series Analysis During the 20th Century. Springer, 161–215.

4. Leo Breiman. 2001. Random forests. Machine learning 45 (2001), 5–32.

5. Shaked Brody, Uri Alon, and Eran Yahav. 2021. How attentive are graph attention networks?arXiv preprint arXiv:2105.14491 (2021).

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