Spatiotemporal Graph

Spatiotemporal graphs represent data with both spatial and temporal dependencies, aiming to model and predict complex dynamic systems. Current research focuses on developing graph neural network architectures, often incorporating convolutional and recurrent layers, to effectively capture these dependencies in various applications. These models are applied to diverse problems, including traffic forecasting, anomaly detection in time series, and multi-agent interaction prediction, improving accuracy and efficiency over traditional methods. The resulting advancements have significant implications for various fields, from transportation optimization and resource management to understanding complex biological and social systems.