Joint predictioN

Joint prediction, encompassing the simultaneous forecasting of multiple related variables, aims to improve accuracy and efficiency compared to predicting each variable independently. Current research focuses on developing advanced architectures like Temporal Fusion Transformers and graph neural networks, often incorporating techniques such as multi-task learning, contrastive learning, and conformal prediction to enhance model performance and uncertainty quantification. This approach holds significant value across diverse fields, from healthcare (predicting vital signs) to autonomous driving (forecasting agent trajectories) and resource management (predicting skill demand and supply), by leveraging inherent correlations between variables for more robust and informative predictions.