提出新型空间平衡注意力机制，实现高效精准的时空预测。
Towards Scalable and Structured Spatiotemporal Forecasting
- 分块建模空间关系，通过局部与全局注意力协同捕捉相关性。
- 在中大型数据集上相较基线提升最高7.7%性能，运行开销低。
- 适合需要可扩展性和结构化建模的时空预测任务。
本文提出一种新颖的时空预测空间平衡注意力模块。为兼顾空间邻近性与全局相关性，将空间图划分为若干子图，利用子图内注意力学习局部空间相关性；通过子图节点聚合生成子图表征，并借助子图间注意力实现跨子图信息传递。基于该模块，构建逐级扩大子图尺度的多尺度时空预测模型。所提模型兼具可扩展性与结构化空间相关性建模能力，且实现简便。在中到大规模真实时空数据集上的实验表明，其性能相较基线方法最高提升7.7%，同时保持较低运行成本。
原文摘要 · Abstract (English)
In this paper, we propose a novel Spatial Balance Attention block for spatiotemporal forecasting. To strike a balance between obeying spatial proximity and capturing global correlation, we partition the spatial graph into a set of subgraphs and instantiate Intra-subgraph Attention to learn local spatial correlation within each subgraph; to capture the global spatial correlation, we further aggregate the nodes to produce subgraph representations and achieve message passing among the subgraphs via Inter-subgraph Attention. Building on the proposed Spatial Balance Attention block, we develop a multiscale spatiotemporal forecasting model by progressively increasing the subgraph scales. The resulting model is both scalable and able to produce structured spatial correlation, and meanwhile, it is easy to implement. We evaluate its efficacy and efficiency against the existing models on real-world spatiotemporal datasets from medium to large sizes. The experimental results show that it can achieve performance improvements up to 7.7% over the baseline methods at low running costs.
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