STACI融合神经网络与概率建模，高效精准地量化时空数据的不确定性。
STACI: Spatio-Temporal Aleatoric Conformal Inference
- 用变分贝叶斯神经网络逼近非平稳时空高斯过程，兼顾灵活性与计算效率。
- 在百万级数据集上仍能生成统计有效的预测区间，优于现有方法。
- 适合需要高精度不确定性估计的时空预测场景，如气象或环境建模。
拟合高斯过程（GPs）可为时空场估计提供可解释的偶然不确定性量化。然而，时空深度学习模型通常假设响应的协方差矩阵为独立形式，无法捕捉底层相关结构；而时空GPs则面临可扩展性差及核函数假设受限带来的近似偏差问题。本文提出STACI，一种新框架，包含对非平稳时空GP的变分贝叶斯神经网络近似，以及一种新型时空合规推断算法。STACI具有高度可扩展性，利用神经网络的GPU训练能力，能够提供统计有效的预测区间以实现不确定性量化。实验表明，STACI在准确逼近时空过程方面优于对比的GPs和深度学习方法，并可轻松扩展至包含数百万观测值的数据集。
原文摘要 · Abstract (English)
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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