发表机构
Potsdam Institute for Climate Impact Research; Technical University of Berlin; University of Illinois at Chicago; Northwestern Polytechnical University; Humboldt University Berlin（波茨坦气候影响研究所; 柏林工业大学; 伊利诺伊大学芝加哥分校; 西北工业大学; 柏林洪堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
WaveGSSM是一种二阶图状态空间模型，通过耦合当前模式与变化率状态，显式建模时空模式传播，在时间图基准和天气预报中达到最优性能，显著降低预测误差。
AI 中文摘要
时空图模型通常使用图神经网络（GNN）对每个快照进行编码，然后通过时间模块连接得到的表示。这种先空间后时间的设计虽然有效，但并未明确表示模式如何在图中移动。我们通过实验证明，对于传播过程，当最近的速率变化不同时，相同的当前场可能导致不同的未来，这促使我们在预测状态中显式表示运动。我们引入了WaveGSSM，一种二阶图状态空间模型，它在每个节点维护两个耦合的潜在状态：一个用于当前模式，另一个用于其时间变化率。图波转换通过图交互更新运动状态，并利用它推进模式状态，在单次展开中将空间传播与时间演化耦合起来。我们在四个时间图基准和全球天气预报上评估了WaveGSSM。它在时间图基准上始终达到最佳平均性能，并且相对于骨干匹配的快照模型，在1至5天天气预报中平均将位势高度均方根误差（RMSE）降低了20.2%，同时更好地保留了大规模大气模式。
英文摘要
Spatio-temporal graph models typically encode each snapshot with a GNN and then connect the resulting representations through a temporal module. This space-then-time design is effective, yet it does not explicitly represent how a pattern moves across the graph. We show empirically that, for a propagating process, the same present field can lead to different futures when its recent rate of change differs, motivating an explicit representation of motion in the predictive state. We introduce WaveGSSM, a second-order graph state-space model that maintains two coupled latent states at each node, one for the current pattern and one for its temporal rate of change. A graph-wave transition updates the motion state through graph interactions and uses it to advance the pattern state, coupling spatial propagation and temporal evolution within a single rollout. We evaluate WaveGSSM on four temporal-graph benchmarks and global weather forecasting. It consistently achieves the best mean performance across the temporal-graph benchmarks and reduces the geopotential RMSE by 20.2% on average for 1- to 5-day weather forecasts relative to a backbone-matched snapshot model, while better preserving large-scale atmospheric patterns.
