Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine learning. A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing. There has been fierce competition and many novel methods have been proposed. In this paper, we present the method of spatio-temporal graph neural controlled differential equation (STG-NCDE). Neural controlled differential equations (NCDEs) are a breakthrough concept for processing sequential data. We extend the concept and design two NCDEs: one for the temporal processing and the other for the spatial processing. After that, we combine them into a single framework. We conduct experiments with 6 benchmark datasets and 20 baselines. STG-NCDE shows the best accuracy in all cases, outperforming all those 20 baselines by non-trivial margins.

翻译：交通流量预测是机器学习领域最受欢迎的时空任务之一,该领域的一种普遍做法是将平面变速网络和时空处理的经常性神经网络结合起来。曾经有过激烈的竞争,并提出了许多新颖的方法。在本文中,我们介绍了时空平面图控制神经差异方程式(STG-NCDE)的方法。神经控制差异方程式(NCDEs)是处理连续数据的一个突破概念。我们扩展了概念并设计了两个NCDEs:一个用于时间处理,另一个用于空间处理。之后,我们将它们合并为一个单一的框架。我们用6个基准数据集和20个基线进行实验。STG-NCDE展示了所有情况下的最佳准确性,用非三边边边边的边距比来完成所有20个基线。