Spatiotemporal Network

Spatiotemporal networks analyze data exhibiting both spatial and temporal dependencies, aiming to model and predict complex dynamic systems. Current research focuses on developing sophisticated architectures, such as graph convolutional networks and transformers, often incorporating attention mechanisms and multi-modal data integration to improve accuracy and efficiency in diverse applications. These advancements are significantly impacting fields like traffic forecasting, environmental monitoring, and video analysis by enabling more accurate predictions and efficient resource allocation. The development of lightweight and interpretable models remains a key area of ongoing investigation.