SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks

SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks

Quantifying uncertainty is crucial for robust and reliable predictions. However, existing spatiotemporal deep learning mostly focuses on deterministic prediction, overlooking the inherent uncertainty in such prediction. Particularly, highly-granular spatiotemporal datasets are often sparse, posing e...

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Bibliographic Details
Main Authors: Zhuang, Dingyi, Bu, Yuheng, Wang, Guang, Wang, Shenhao, Zhao, Jinhua
Other Authors: Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Format: Article
Language:English
Published: ACM|The 32nd ACM International Conference on Advances in Geographic Information Systems 2024
Online Access:https://hdl.handle.net/1721.1/157749

Internet

https://hdl.handle.net/1721.1/157749

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