This article is about 11,000 words long and is recommended to be read in over 10 minutes. This is a survey overview of large models for time series and spatio-temporal data. Time-related data, especially time series and spatio-temporal data, are ubiquitous in real-world applications. These data capture measurements of dynamic systems and are generated in … Read more
Introduction Graph neural networks (GNNs) have been widely applied to the modeling and representation learning of graph-structured data. However, mainstream research has been limited to handling static network data, while real complex networks often undergo structural and property evolution over time. The team led by Katarzyna at the University of Technology Sydney recently published a … Read more
Spatio-temporal data is the foundation of many intelligent applications, revealing the causal relationships between measurements at a specific location and historical data from the same or other locations. In this context, Adaptive Spatio-Temporal Graph Neural Networks (ASTGNNs) have emerged as a powerful tool for modeling these dependencies, particularly through data-driven approaches rather than predefined spatial … Read more