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arXiv · 2606.16356

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

Andro Sabashvili
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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.

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Andro Sabashvili. 2026-06-15. Simulation-Augmented Multi-Step Split Conformal Prediction for Aggregated Forecasts. https://arxiv.org/abs/2606.16356

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