Description

This Master Prompt focuses on probabilistic forecasting methodologies that quantify uncertainty, represent possible future outcomes, and support risk-aware decisions beyond conventional point forecasts. It covers predictive probability distributions, quantile forecasting, prediction intervals, Bayesian forecasting, Bayesian networks, probabilistic graphical models, Monte Carlo simulation, bootstrap methods, conditional density estimation, kernel density estimation, copula-based dependence modelling, and generative probabilistic approaches. The theme addresses aleatoric and epistemic uncertainty, parameter uncertainty, distributional forecasting, heteroscedasticity, tail behaviour, extreme-event forecasting, uncertainty propagation, interval calibration, forecast reconciliation, and post-processing techniques. It further examines probabilistic scoring rules, continuous ranked probability scores, pinball loss, interval scores, calibration diagnostics, reliability analysis, sharpness, and decision-relevant risk measures. Applications include renewable energy forecasting, electricity price volatility, financial risk, flood forecasting, environmental extremes, and operational systems requiring reliable uncertainty estimates.