  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.
