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About this paper
Wenchong He, Zhe Jiang
This paper by Wenchong He and Zhe Jiang from the University of Florida surveys uncertainty quantification methods for deep learning models applied to spatiotemporal data. It details unique challenges such as violated independence assumptions and non-stationarity, while categorizing existing approaches for both aleatoric and epistemic uncertainty.
Key insights
- Spatiotemporal data's inherent dependencies (spatial autocorrelation, temporal links) fundamentally challenge standard uncertainty quantification assumptions.
- Uncertainty in spatiotemporal deep learning arises from both irreducible data noise (aleatoric) and model limitations (epistemic), each requiring distinct quantification approaches.
- Addressing issues like non-stationarity, multi-resolution data, and computational efficiency are crucial for robust spatiotemporal uncertainty quantification.
Summary by alphaXiv.
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