EMBER: EVIDENCE-DRIVEN MODAL BOUNDARY EVOLUTION FOR SPATIO-TEMPORAL FORECASTING

Spatio-temporal forecasting requires a coordinated transformation from an observed relational field to a future whose spatial organization, regime evolution, temporal memory, and physical scale remain mutually consistent. We introduce EMBER (Evidence-driven Modal Boundary Evolution and Readout), which formulates this task as a coupled boundary-to-future operator. The history and graph first form a coordinated boundary state; this state conditions a node-wise rupture geometry, direct multi-horizon evolution, and a sample-level fractional memory law, while a robust boundary fiber restores local physical units. Our dependency-aligned theoretical analysis establishes stable Galerkin coordination, exact rupture decomposition with a retained orthogonal complement, a quantifiable evidence barrier governing modal reversal and coordinate-wise dynamic regimes, and positive power-law memory with sublinear accumulation and scale-covariant reconstruction. These results show that EMBER preserves boundary information while evolving it through controlled history-dependent dynamics and returning forecasts consistently to local physical units. Mechanism diagnostics further confirm well-conditioned and active Galerkin coordinates together with boundary-dependent fractional orders. Across thirteen graph, grid, and weather tasks, including temperature, humidity, and 12-hour wind-speed forecasting, EMBER achieves the strongest overall performance among the compared methods.