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 …

Deep learning models are modern tools for spatio-temporal graph (STG) forecasting.
Though successful, we argue that data scarcity is a key factor limiting their recent …

Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous, and non-
stationary, leading to the continuous challenge of spatial-temporal graph learning. In the …

Many real-world ubiquitous applications, such as parking recommendations and air
pollution monitoring, benefit significantly from accurate long-term spatio-temporal …

Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for
spatio-temporal graph (STG) forecasting. Despite their success, they fail to model intrinsic …

Spatio-temporal prediction on multivariate time series has received tremendous attention for
extensive applications in the real world, where the dynamic unknown spatio-temporal …

Spatiotemporal graph neural networks have shown to be effective in time series forecasting
applications, achieving better performance than standard univariate predictors in several …

The spatio-temporal graph learning is becoming an increasingly important object of graph
study. Many application domains involve highly dynamic graphs where temporal information …

Multivariate time-series forecasting is a critical task for many applications, and graph time-
series network is widely studied due to its capability to capture the spatial-temporal …

Spatial-temporal network data forecasting is of great importance in a huge amount of
applications for traffic management and urban planning. However, the underlying complex …