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 27 baselines. STG-NRDE shows the best accuracy in all cases, outperforming all those 27 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。主流方法往往将图卷积网络与循环神经网络结合以进行时空处理。该领域竞争激烈，已有许多新颖方法被提出。本文提出了时空图神经粗糙微分方程方法（STG-NRDE）。神经粗糙微分方程（NRDE）是处理时间序列数据的一个突破性概念，其核心思想是利用对数签名变换将时间序列样本转换为相对较短的 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，随后将它们整合到一个统一框架中。我们基于6个基准数据集和27个基线方法进行了实验。结果表明，STG-NRDE在所有情况下均达到最高精度，并以显著优势超越了这27种基线方法。