Towards Efficient, Scalable, and Transferable Spatio-Temporal Forecasting: A Periodic Perspective

Transferable spatio-temporal forecasting seeks to reuse knowledge from data-rich regions when target observations are scarce. Recurring patterns offer transferable information, yet their signal levels, fluctuation amplitudes, and short-term evolution vary across regions. We propose PACT, a graph-free forecasting framework that uses recent observations and future daily-cycle positions to guide the application of shared temporal regularities. PACT predicts local changes relative to each node's latest observation in a normalized space, then refines these predictions using information aggregated across nodes. Its core mechanism conditions a compact global summary on both the future cycle position and the current regional state, enabling node-specific retrieval of context for each forecast step. Shared parameters accommodate different node sets without adjacency matrices or cross-region node correspondence. The compact aggregation and retrieval operations avoid pairwise node interactions, yielding computation that scales linearly with node count at fixed model dimensions. Experiments on real-world traffic datasets demonstrate strong forecasting performance across regions and sensing scales, together with efficient target adaptation and inference. Code is available at https://anonymous.4open.science/r/PACT-F67B/.