Spatiotemporal Predictive Learning

Spatiotemporal predictive learning aims to forecast future states of dynamic systems by learning from sequential data, encompassing both spatial and temporal dependencies. Current research heavily focuses on developing efficient and accurate models, shifting away from recurrent neural networks towards transformer-based architectures and convolutional networks with attention mechanisms to improve parallelization and scalability. This field is crucial for advancing applications in diverse areas such as traffic prediction, weather forecasting, and autonomous driving, where accurate and timely predictions are essential. The development of simpler, yet powerful baselines alongside more sophisticated models is driving progress towards more robust and efficient solutions.