TY - JOUR AU - Choi, Jeongwhan AU - Choi, Hwangyong AU - Hwang, Jeehyun AU - Park, Noseong PY - 2022/06/28 Y2 - 2024/03/28 TI - Graph Neural Controlled Differential Equations for Traffic Forecasting JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 36 IS - 6 SE - AAAI Technical Track on Machine Learning I DO - 10.1609/aaai.v36i6.20587 UR - https://ojs.aaai.org/index.php/AAAI/article/view/20587 SP - 6367-6374 AB - 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. ER -