CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting

Zhengyang Zhou, Jiahao Shi, Hongbo Zhang, Qiongyu Chen, Xu Wang, Hongyang Chen, Yang Wang. CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting. In Luz Angelica Caudillo-Mata, Silvio Lattanzi, Andrés Muñoz Medina, Leman Akoglu, Aristides Gionis, Sergei Vassilvitskii, editors, Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024. pages 985-993, ACM, 2024. [doi]

@inproceedings{ZhouSZCWCW24,
  title = {CreST: A Credible Spatiotemporal Learning Framework for Uncertainty-aware Traffic Forecasting},
  author = {Zhengyang Zhou and Jiahao Shi and Hongbo Zhang and Qiongyu Chen and Xu Wang and Hongyang Chen and Yang Wang},
  year = {2024},
  doi = {10.1145/3616855.3635759},
  url = {https://doi.org/10.1145/3616855.3635759},
  researchr = {https://researchr.org/publication/ZhouSZCWCW24},
  cites = {0},
  citedby = {0},
  pages = {985-993},
  booktitle = {Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024},
  editor = {Luz Angelica Caudillo-Mata and Silvio Lattanzi and Andrés Muñoz Medina and Leman Akoglu and Aristides Gionis and Sergei Vassilvitskii},
  publisher = {ACM},
}