<ul data-eligibleForWebStory="true"><li>STOAT (Spatial-Temporal Probabilistic Causal Inference Network) is a novel framework for probabilistic forecasting in spatial-temporal causal time series (STC-TS) with region-specific temporal observations driven by causally relevant covariates.</li><li>The proposed method incorporates a spatial relation matrix to encode interregional dependencies, improving spatially informed causal effect estimation and calibrated uncertainty modeling.</li><li>STOAT utilizes deep probabilistic models to estimate distribution parameters and explores multiple output distributions to capture region-specific variability.</li><li>Experiments on COVID-19 data from six countries show that STOAT outperforms existing probabilistic forecasting models like DeepAR, DeepVAR, and Deep State Space Model, especially in regions with strong spatial dependencies.</li><li>The framework bridges causal inference and geospatial probabilistic forecasting, offering a versatile approach for complex spatial-temporal tasks such as epidemic management.</li></ul>

STOAT: Spatial-Temporal Probabilistic Causal Inference Network

Discover more