Simulation-Augmented Multi-Step Split Conformal Prediction for Aggregated Forecasts

Abstract:We study uncertainty quantification for aggregated forecasting tasks such as annual totals and year-over-year growth rates. We propose SA-MSCP, a simulation-augmented multi-step split conformal method that generates future paths from cross-validated residuals using a block bootstrap and constructs prediction intervals from empirical quantiles. Experiments show that SA-MSCP improves empirical coverage over a simulated-path baseline for aggregated and growth-rate targets. Our results demonstrate that simulation-enhanced conformal calibration is an effective and general framework for uncertainty quantification in aggregated time-series forecasting.

Machine Learning (cs.LG)

arXiv:2606.16356 [cs.LG]

(or arXiv:2606.16356v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.16356 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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