From Values to Interactions: Adaptive Graph Pre-training for Spatiotemporal Forecasting

Spatiotemporal forecasting is inherently relational: future states depend not only on historical observations but also on how information is organized across interacting entities. Yet existing forecasting models typically learn such relations within task-specific predictors, while recent pre-training methods remain largely value-centric, emphasizing temporal reconstruction without explicitly encoding reusable cross-entity dependencies. We propose AGPST, an interaction-aware self-supervised framework that treats relational structure as reusable pre-training knowledge. AGPST combines data-adaptive graph aggregation with masked temporal modeling, enabling masked states to be reconstructed from both cross-entity evidence and long-range historical context. The resulting representations capture complementary relational and temporal information and can be transferred to heterogeneous downstream predictors through lightweight feature integration without modifying their internal architectures. Experiments on six traffic forecasting benchmarks show consistent improvements over representative task-specific and pre-training baselines across datasets, metrics, and prediction horizons. Ablation and cross-architecture studies further demonstrate the complementary benefits of adaptive interaction learning, graph propagation, and masked reconstruction, supporting the effectiveness and reusability of the learned representations.