Ensemble Prediction

Ensemble prediction methods combine multiple models to improve accuracy, robustness, and uncertainty quantification in diverse prediction tasks. Current research emphasizes dynamic weighting of model predictions, exploring architectures like neural networks and diffusion probabilistic models, and employing techniques like sharpness minimization and weighted scoring rules to enhance performance, particularly for extreme events and noisy data. These advancements are significant for improving the reliability and interpretability of predictions across various fields, from weather forecasting and materials science to medical diagnostics and natural language processing.