发表机构
The Hong Kong University of Science and Technology (Guangzhou); Chang’an University; China University of Geosciences（香港科技大学（广州）; 长安大学; 中国地质大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现实交通数据时空预测难题，提出STKAN架构，将泰勒多项式柯尔莫哥洛夫-阿诺德网络模块引入时空令牌混合，经实验验证其性能有竞争力，表明非线性函数逼近器设计对时空预测架构设计有补充作用。
AI 中文摘要
现实世界的交通数据呈现出异质的空间相关性和非线性的时间动态，这给准确的时空预测带来了巨大挑战。现有方法已经开发出越来越复杂的图、注意力和分解架构，而底层非线性函数逼近器的影响相对较少受到关注。在这项工作中，我们提出了STKAN，一种时空预测架构，它将泰勒多项式柯尔莫哥洛夫-阿诺德网络模块引入到空间和时间令牌混合中。STKAN首先通过可学习的软节点组分配机制构建高级空间表示，应用组内空间混合，随后对压缩序列上的时间依赖性进行建模。还使用了空间和时间自注意力层来捕获长程交互。在五个交通预测基准上的实验表明，STKAN实现了有竞争力的性能，并且在测试设置中比评估的基于MLP的变体表现更好。这些结果表明，非线性函数逼近器的设计可以作为时空预测中架构设计的有用补充。
英文摘要
Real-world traffic data exhibit heterogeneous spatial correlations and nonlinear temporal dynamics, posing substantial challenges for accurate spatio-temporal forecasting. Existing approaches have developed increasingly sophisticated graph, attention, and decomposition architectures, while the influence of the underlying nonlinear function approximator has received comparatively less attention. In this work, we propose STKAN, a spatio-temporal forecasting architecture that introduces Taylor-polynomial Kolmogorov--Arnold Network modules into spatial and temporal token mixing. STKAN first constructs high-level spatial representations through a learnable soft node-group assignment mechanism, applies group-wise spatial mixing, and subsequently models temporal dependencies over the compressed sequence. Spatial and temporal self-attention layers are further employed to capture long-range interactions. Experiments on five traffic forecasting benchmarks show that STKAN achieves competitive performance and performs better than the evaluated MLP-based variant in the tested settings. These results suggest that the design of nonlinear function approximators can serve as a useful complement to architectural design in spatio-temporal forecasting.
