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 rough differential equation (STG-NRDE). Neural rough differential equations (NRDEs) are a breakthrough concept for processing time-series data. Their main concept is to use the log-signature transform to convert a time-series sample into a relatively shorter series of feature vectors. We extend the concept and design two NRDEs: 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 21 baselines. STG-NRDE shows the best accuracy in all cases, outperforming all those 21 baselines by non-trivial margins.

翻译：交通预测是机器学习领域中最流行的时空任务之一。该领域的主流方法是将图卷积网络与循环神经网络结合，以处理时空数据。相关研究竞争激烈，众多新方法已被提出。本文提出了时空图神经粗糙微分方程（STG-NRDE）方法。神经粗糙微分方程（NRDE）是一种处理时间序列数据的突破性概念，其核心思想是利用对数签名变换将时间序列样本转换为长度更短的特征向量序列。我们扩展了这一概念，设计了两种NRDE：一种用于时间处理，另一种用于空间处理，随后将二者整合到统一框架中。我们在6个基准数据集和21个基线方法上进行了实验。STG-NRDE在所有情况下均展现出最优精度，并以显著优势超越了全部21个基线方法。