Graph Neural Rough Differential Equations for Traffic Forecasting

• URL: http://arxiv.org/abs/2303.10909v2
• Date: Tue, 6 Jun 2023 08:11:51 GMT
• Title: Graph Neural Rough Differential Equations for Traffic Forecasting
• Authors: Jeongwhan Choi, Noseong Park
• Abstract summary: We present the rough method of graph neural differential equation (STG-NRDE) NRDE is a breakthrough concept for processing time-series data. We conduct experiments with 6 benchmark datasets and 27 baselines.
• Score: 12.920211720852173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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