@article{Choi_Choi_Hwang_Park_2022, title={Graph Neural Controlled Differential Equations for Traffic Forecasting}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/20587}, DOI={10.1609/aaai.v36i6.20587}, abstractNote={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.}, number={6}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Choi, Jeongwhan and Choi, Hwangyong and Hwang, Jeehyun and Park, Noseong}, year={2022}, month={Jun.}, pages={6367-6374} }