As a crucial technique for developing a smart city, traffic forecasting has
become a popular research focus in academic and industrial communities for
decades. This task is highly challenging due to complex and dynamic
spatial-temporal dependencies in traffic networks. Existing works ignore
continuous temporal dependencies and spatial dependencies evolving over time.
In this paper, we propose Continuously Evolving Graph Neural Controlled
Differential Equations (CEGNCDE) to capture continuous temporal dependencies
and spatial dependencies over time simultaneously. Specifically, a continuously
evolving graph generator (CEGG) based on NCDE is introduced to generate the
spatial dependencies graph that continuously evolves over time from discrete
historical observations. Then, a graph neural controlled differential equations
(GNCDE) framework is introduced to capture continuous temporal dependencies and
spatial dependencies over time simultaneously. Extensive experiments
demonstrate that CEGNCDE outperforms the SOTA methods by average 2.34% relative
MAE reduction, 0.97% relative RMSE reduction, and 3.17% relative MAPE
reduction.

提出了连续演化图神经控制微分方程（CEGNCDE）来同时捕捉连续的时间依赖关系和随时间演变的空间依赖关系，通过基于 NCDE 的连续演化图生成器（CEGG）来生成连续演化的离散历史观测下的空间依赖图，然后引入了图神经控制微分方程（GNCDE）框架来同时捕捉连续的时间依赖关系和空间依赖关系。大量实验表明，CEGNCDE 相较于现有方法平均减少了 2.34％的相对 MAE，0.97％的相对 RMSE 和 3.17％的相对 MAPE。