Forecast Distribution

Forecast distribution research aims to move beyond simple point predictions by generating probability distributions representing the uncertainty inherent in forecasting various phenomena, from electricity demand to weather patterns and healthcare wait times. Current efforts focus on developing and refining machine learning models, including neural networks (e.g., normalizing flows, deep distribution regression), boosted generalized normal distributions, and Bayesian approaches, often incorporating graph structures to capture relationships between time series or leveraging ensemble methods for improved accuracy and calibration. These advancements are crucial for enhancing decision-making under uncertainty across diverse fields, improving risk management, and leading to more reliable and informative forecasts.