Recurrent graph convolutional neural networks are highly effective machine learning
techniques for spatiotemporal signal processing. Newly proposed graph neural network …

Graph processes model a number of important problems such as identifying the epicenter of
an earthquake or predicting weather. In this paper, we propose a Graph Convolutional …

Traffic forecasting has always been an important part of intelligent transportation systems. At
present, spatiotemporal graph neural networks are widely used to capture spatiotemporal …

With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

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 …

Devising and analysing learning models for spatiotemporal network data is of importance for
tasks including forecasting, anomaly detection, and multi-agent coordination, among others …

Traffic forecasting plays an important role of modern Intelligent Transportation Systems (ITS).
With the recent rapid advancement in deep learning, graph neural networks (GNNs) have …

For multivariate time series forecasting problems, entirely using the dependencies between
series is a crucial way to achieve accurate forecasting. Real-life multivariate time series …

Being a kind of non-Euclidean data, spatiotemporal graph data exists everywhere from traffic
flow, air quality index to crime case, etc. Unlike the raster data, the irregular and disordered …

Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …