ProST: Learning to Progressively Refine Spatio-Temporal Forecasts in a Multi-Scale Forecast Space with Adaptive Scale Selection

Spatio-temporal forecasting supports real-world applications such as traffic management and air-quality monitoring. Many existing methods model historical temporal dynamics and spatial dependencies to directly predict the future at the target scale. This approach leaves the relationship between coarse future structure and finer detail implicit, without explicit control over how each is revised during forecasting. We introduce ProST (Progressive Spatio-Temporal Forecasting) to progressively refine predictions of the same future in a joint temporal–spatial scale space. ProST controls coarse-scale revision separately from the addition of finer-scale detail, while adaptive scale selection determines the scales at which refinement proceeds. Each selected scale pair defines an explicit forecast state and a scale-aligned target. Refinement expands the preceding forecast and decomposes a learned correction into a weighted coarse-scale update and zero-aggregate detail. A shared scale-conditioned neural forecaster computes these corrections from historical observations and the preceding forecast. A history-conditioned selector chooses a complete scale-selection sequence before forecasting begins. The refinement satisfies an exact cross-scale identity relating the refined forecast's aggregate to the updated coarse prediction. Experiments show positive mean intermediate refinement gains, and frozen-weight interventions on the temporal parent/detail decomposition increase terminal error when either component is removed across all evaluated seeds and datasets. Our code is available in https://anonymous.4open.science/r/ProST-1B6C/