STACI: Spatio-Temporal Aleatoric Conformal Inference

• URL: http://arxiv.org/abs/2505.21658v2
• Date: Thu, 23 Oct 2025 22:31:52 GMT
• Title: STACI: Spatio-Temporal Aleatoric Conformal Inference
• Abstract summary: STACI is highly scalable and takes advantage of training capabilities for neural network models.<n>It outperforms competing deep methods in accurately approxinging-temporal processes.
• Score: 12.74402506745844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fitting Gaussian Processes (GPs) provides interpretable aleatoric uncertainty quantification for estimation of spatio-temporal fields. Spatio-temporal deep learning models, while scalable, typically assume a simplistic independent covariance matrix for the response, failing to capture the underlying correlation structure. However, spatio-temporal GPs suffer from issues of scalability and various forms of approximation bias resulting from restrictive assumptions of the covariance kernel function. We propose STACI, a novel framework consisting of a variational Bayesian neural network approximation of non-stationary spatio-temporal GP along with a novel spatio-temporal conformal inference algorithm. STACI is highly scalable, taking advantage of GPU training capabilities for neural network models, and provides statistically valid prediction intervals for uncertainty quantification. STACI outperforms competing GPs and deep methods in accurately approximating spatio-temporal processes and we show it easily scales to datasets with millions of observations.

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