FACO: Transferring Cross-City Learning Experience via Spatiotemporal Relay Function

Cross-city spatiotemporal forecasting aims to transfer predictive experience from data-rich cities to targets with limited observations. Existing methods typically assume continued access to source data, models, or computation, which may fail when historical sources and future targets are temporally separated. We study Forecasting spatiotemporal observations Across Cities Offline (FACO), where source-side learning is completed before the target becomes available and source-private observations, forecasting systems, and source-side computation are inaccessible during target adaptation. This setting raises three challenges: preserving predictive experience beyond source availability, maintaining consistent knowledge across heterogeneous cities and forecasting systems, and identifying trustworthy historical dynamics from limited target evidence. We develop FACO as an offline cross-city forecasting framework that aligns heterogeneous forecasting systems in predictive-functional coordinates, distills source-conditioned temporal evolution, preserves transferable dynamics through a frozen cross-city relay function, and adapts the relay to a future target using functional trust and closed-loop evolution. The relay is realized through a Spatiotemporal Relay Function (SRF), which enables source-derived dynamics to remain usable after source exit. Experiments across heterogeneous cities and forecasting architectures show that FACO achieves the strongest overall performance among the evaluated approaches, reducing MAE by 29.7% over STGP on Innsbruck under the three-day target adaptation setting. These results support representing cross-city predictive experience as source-independent dynamics that remain reusable beyond the availability and architecture of their source systems.