Interval Prediction

Interval prediction focuses on generating a range of values, rather than a single point estimate, to represent the uncertainty inherent in predictions. Current research emphasizes improving the accuracy and coverage of these prediction intervals across diverse applications, employing various methods including quantile regression, Bayesian neural networks, and generative adversarial networks, often coupled with techniques like bootstrapping or conformal prediction to ensure reliable uncertainty quantification. This enhanced uncertainty estimation is crucial for improving decision-making in fields ranging from financial forecasting and energy management to healthcare diagnostics and manufacturing, where understanding the range of possible outcomes is vital.