The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

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 …

Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic
forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the …

Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the
barriers to economic growth. Forecasting air quality has become an increasingly important …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

Machine learning (ML) is powering a rapidly-increasing number of web applications. As a
crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model …

Leading graph ordinary differential equation (ODE) models have offered generalized
strategies to model interacting multi-agent dynamical systems in a data-driven approach …