CyPhy-STN: Cycle-aware Physics-inspired Message Passing for Multivariate Spatio-Temporal Forecasting

Can a single physics-inspired architecture perform consistently across diverse time-series prediction tasks governed by different dynamics? We introduce the Cycle-aware Physics-inspired Spatio-Temporal Network (CyPhy-STN), a unified architecture for multivariate spatio-temporal forecasting. It combines cyclic temporal attention with parallel, mechanism-specific graph propagation inspired by continuity, viscosity, relaxation, advection, and reaction. Learnt channel- and regime-aware routing determines which physical variables enter each mechanism and how messages are propagated, allowing the same architecture to be applied across different domains without the need of a fixed governing equation or an architectural redesign. Across four traffic and atmospheric forecasting benchmarks, CyPhy-STN achieves lower cross-benchmark regret and average rank than state-of-the-art spatio-temporal models with a low parameter count.