Simulation-Augmented Multi-Step Split Conformal Prediction for Aggregated Forecasts
Researchers have developed a new method called Simulation-Augmented Multi-Step Split Conformal Prediction (SA-MSCP) to improve uncertainty quantification in aggregated forecasting tasks. This technique generates future paths using a block bootstrap from cross-validated residuals and constructs prediction intervals from empirical quantiles. Experiments indicate that SA-MSCP enhances empirical coverage compared to existing baselines, demonstrating its effectiveness for aggregated time-series forecasting. AI