剪枝以提升泛化能力：一种面向转移的时空图框架
Pruning for Generalization: A Transfer-Oriented Spatiotemporal Graph Framework
AI总结：
TL-GPSTGN通过结构感知的上下文剪枝提升时空预测模型的泛化能力与样本效率。
摘要：
在图结构领域中，多变量时间序列预测对于现实应用至关重要，然而现有的时空模型在数据稀缺和跨领域转移时往往会出现性能下降。我们通过结构感知的上下文选择视角来解决这些挑战。我们提出了TL-GPSTGN，一种面向转移的时空框架，通过选择性地剪枝非优化的图上下文来提高样本效率和分布外泛化能力。具体而言，我们的方法采用信息论和相关性为基础的准则来提取结构信息丰富的子图和特征，从而得到紧凑且语义基础的表示。这种优化的上下文随后被整合到时空卷积架构中，以捕捉复杂的多变量动态。在大规模交通基准测试中，TL-GPSTGN在低数据转移场景中始终优于基线模型。我们的发现表明，显式的上下文剪枝作为一种强大的归纳偏置，有助于提高基于图的预测模型的鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
