用模拟路径提升聚合预测的置信区间准确性
Simulation-Augmented Multi-Step Split Conformal Prediction for Aggregated Forecasts
- 通过块自助法从验证残差生成未来路径
- 在聚合目标上实现比基线更高的覆盖率
- 适合需要可靠不确定性估计的时序预测场景
我们研究了年度总量和同比增长率等聚合预测任务中的不确定性量化问题。提出SA-MSCP方法，利用交叉验证残差进行块自助法生成未来路径，并基于经验分位数构建预测区间。实验表明，该方法在聚合目标和增长率目标上均优于模拟路径基线，实现了更高的实际覆盖概率。结果证明，模拟增强的共形校准是一种有效且通用的聚合时间序列预测不确定性量化框架。
原文摘要 · Abstract (English)
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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