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.

翻译：我们研究了针对年总量和同比增长率等聚合预测任务的不确定性量化问题。提出了一种名为SA-MSCP的仿真增强多步分割保形方法，该方法通过块自举法从交叉验证残差中生成未来路径，并基于经验分位数构建预测区间。实验表明，对于聚合目标与增长率目标，SA-MSCP在经验覆盖概率上优于仿真路径基准方法。我们的结果证明，仿真增强的保形校准是聚合时间序列预测中不确定性量化的有效通用框架。