Pruning for Generalization: A Transfer-Oriented Spatiotemporal Graph Framework

• URL: http://arxiv.org/abs/2602.04153v1
• Date: Wed, 04 Feb 2026 02:41:29 GMT
• Title: Pruning for Generalization: A Transfer-Oriented Spatiotemporal Graph Framework
• Authors: Zihao Jing, Yuxi Long, Ganlin Feng,
• Abstract summary: We propose TL-GPSGNT to improve graph-based time series forecasting models.<n>We use information-theoretic and correlation-based criteria to extract structurally informative subgraphs.<n>We show that TL-GPSGNT consistently outperforms baselines in low-data transfer scenarios.
• Score: 0.6435984242701042
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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