Robust Spatio-Temporal Centralized Interaction for OOD Learning

Recently, spatiotemporal graph convolutional networks have achieved dominant performance in spatiotemporal prediction tasks. However, most models relying on node-to-node messaging interaction exhibit sensitivity to spatiotemporal shifts, encountering out-of-distribution (OOD) challenges. To address these issues, we introduce S patio- T emporal O OD P rocessor (STOP), which employs a centralized messaging mechanism along with a message perturbation mechanism to facilitate robust spatiotemporal interactions. Specifically, the centralized messaging mechanism integrates Context-Aware Units for coarse-grained spatiotemporal feature interactions with nodes, effectively blocking traditional node-to-node messages. We also implement a message perturbation mechanism to disrupt this messaging process, compelling the model to extract generalizable contextual features from generated variant environments. Finally, we customize a spatiotemporal distributionally robust optimization approach that exposes the model to challenging environments, thereby further enhancing its generalization capabilities. Compared with 14 baselines across six datasets, STOP achieves up to 17. 01% improvement in generalization performance and 18. 44% improvement in inductive learning performance. The code is available at https: //github. com/PoorOtterBob/STOP.

Authors

• Jiaming Ma
• Binwu Wang
• Pengkun Wang 0001
• Zhengyang Zhou
• Xu Wang 0029
• Yang Wang 0015

Keywords

• Spatiotemporal learning
• out-of-distribution
• spatiotemporal prediction