Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-
temporal patterns on dynamic graphs. However, existing works fail to generalize under …

Efficient and effective multivariate time series (MTS) forecasting is critical for real-world
applications, such as traffic management and energy dispatching. Most of the current deep …

Societal and industrial infrastructures and systems increasingly leverage sensors that emit
correlated time series. Forecasting of future values of such time series based on recorded …

The way humans travel and even their daily commute, is gradually expanding beyond the
confines of counties and cities. Traffic between counties, cities, and even across the entire …

Spatiotemporal forecasting in various domains, like traffic prediction and weather
forecasting, is a challenging endeavor, primarily due to the difficulties in modeling …

Spatio-temporal forecasting is a critical component of various smart city applications, such
as transportation optimization, energy management, and socio-economic analysis. Recently …

Traffic forecasting plays a key role in Intelligent Transportation Systems, and significant
strides have been made in this field. However, most existing methods can only predict up to …

Because of the complexity of the traffic network and the non-linearity of traffic data, it is
extremely challenging to accurately predict long-term traffic flow. Spatio-temporal graph …

Cyber-physical systems (CPS) widely exists in modern industrial and social infrastructures
and play an important role in realizing their intelligence. A CPS usually contains multiple …

Ubiquitous sensors and mobile devices have spurred the growth of Web-of-Things (WoT)
services in smart cities, making accurate spatio-temporal traffic predictions increasingly …