Title: Orion: Intervention-Validated Causal Discovery for Spatiotemporal Forecasting
Primary area: learning on time series and dynamical systems
Keywords: spatiotemporal forecasting, causal discovery, graph attention networks, do-interventions, multi-head attention

Abstract: While spatiotemporal forecasting plays a critical role in domains such as intelligent transportation and infectious disease prediction, existing methods still face fundamental challenges in modeling complex dependencies in graph-structured data and multi-scale temporal dynamics, primarily because they neglect the inherent causal mechanisms in spatiotemporal propagation processes. To address these limitations, we propose Orion, a framework that integrates trainable causal inference with attention mechanisms, optimized through progressive training. Our key contributions include: (1) the TE-CausGAT module, which jointly performs causal discovery and validates learned structures via do-interventions; (2) the Belt Block architecture, which processes hourly, daily, and weekly patterns in parallel; (3) intelligent data retrieval using a fine-tuned large language model; and (4) a progressive three-stage training strategy that progressively transitions from feature learning to causal discovery to end-to-end optimization. Orion achieves new state-of-the-art performance across multiple benchmarks: in traffic forecasting, it improves MAE by 6.60% on METR-LA, 8.65% on PEMS-08, and achieves optimal results across multiple prediction horizons on PEMS-04; in epidemic forecasting, it improves MAE by 22.70% on CA and 37.73% on TX. Furthermore, comprehensive ablation studies, Dream3 causal validation experiments, and METR-LA case studies collectively demonstrate Orion's effectiveness in both accurate prediction and reliable causal discovery.

TL;DR: We propose Orion, a spatiotemporal forecasting model that combines trainable causal inference with attention mechanisms, along with progressive curriculum learning and large language model-enhanced data retrieval.