🤖 AI Summary
This work addresses the performance degradation of graph-structured multivariate time series forecasting under data-scarce and cross-domain transfer scenarios by proposing a structure-aware context selection mechanism within a transfer-oriented spatiotemporal graph learning framework. The approach incorporates an explicit graph context pruning strategy as an inductive bias, leveraging information-theoretic measures and correlation-based criteria to select informative subgraphs and features. This selection module is seamlessly integrated into a spatiotemporal convolutional architecture, enhancing both sample efficiency and out-of-distribution generalization. Evaluated under low-data transfer settings on large-scale traffic benchmark datasets, the proposed model significantly outperforms existing baseline methods.
📝 Abstract
Multivariate time series forecasting in graph-structured domains is critical for real-world applications, yet existing spatiotemporal models often suffer from performance degradation under data scarcity and cross-domain shifts. We address these challenges through the lens of structure-aware context selection. We propose TL-GPSTGN, a transfer-oriented spatiotemporal framework that enhances sample efficiency and out-of-distribution generalization by selectively pruning non-optimized graph context. Specifically, our method employs information-theoretic and correlation-based criteria to extract structurally informative subgraphs and features, resulting in a compact, semantically grounded representation. This optimized context is subsequently integrated into a spatiotemporal convolutional architecture to capture complex multivariate dynamics. Evaluations on large-scale traffic benchmarks demonstrate that TL-GPSTGN consistently outperforms baselines in low-data transfer scenarios. Our findings suggest that explicit context pruning serves as a powerful inductive bias for improving the robustness of graph-based forecasting models.