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 …

Time series forecasting involves justifying assertions scientifically regarding potential states
or predicting future trends of an event based on historical data recorded at various time …

Sensors in cyber-physical systems often capture interconnected processes and thus emit
correlated time series (CTS), the forecasting of which enables important applications …

Deep learning approaches, given their low cost and high reliability, have gained much
popularity in different subjects, such as computer vision and natural language processing …

Traffic flow prediction plays an instrumental role in modern intelligent transportation systems.
Numerous existing studies utilize inter-embedded fusion routes to extract the intrinsic …

Deep learning has achieved remarkable success in graph-related tasks, yet this
accomplishment heavily relies on large-scale high-quality annotated datasets. However …

Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many
real-world applications. The core issue in MTS forecasting is how to effectively model …

Correlated time series (CTS) forecasting is essential in many practical applications, such as
traffic management and server load control. Various deep learning based solutions have …

Multivariate time series forecasting is of great importance in a diverse range of domains. In
recent years, a variety of spatial-temporal graph neural networks (STGNNs) have been …

Deep recommender systems (DRS) are critical for current commercial online service
providers, which address the issue of information overload by recommending items that are …