Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

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 …

Travel-time prediction constitutes a task of high importance in transportation networks, with
web mapping services like Google Maps regularly serving vast quantities of travel time …

A variety of real-world applications rely on far future information to make decisions, thus
calling for efficient and accurate long sequence multivariate time series forecasting. While …

In recent years, various deep learning architectures have been proposed to solve complex
challenges (eg spatial dependency, temporal dependency) in traffic domain, which have …

Traffic time series forecasting is challenging due to complex spatio-temporal dynamics-time
series from different locations often have distinct patterns; and for the same time series …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Correlated time series (CTS) forecasting plays an essential role in many cyber-physical
systems, where multiple sensors emit time series that capture interconnected processes …

We propose variational quasi-recurrent autoencoders (VQRAEs) to enable robust and
efficient anomaly detection in time series in unsupervised settings. The proposed VQRAEs …

Predicting air quality in fine spatiotemporal granularity is of great importance for air pollution
control and urban sustainability. However, existing studies are either focused on predicting …