AI 中文总结
提出基于 Dirichlet 分位数 Bootstrap 的时间序列重采样方法 DPQBootstrap，通过模拟与实证表明其优于最大熵 Bootstrap，并能提升贝叶斯 ARMA 预测精度与区间质量。
AI 中文摘要
本文提出了 DPQBootstrap，一种基于 Dirichlet 分位数 Bootstrap 和基于秩的时间重构的时间序列概率重采样方法。该方法旨在生成保留原始序列主要特征的伪序列，包括变异性、分位数、极值以及部分时间依赖性。一项模拟研究在八个模拟场景下，将 DPQBootstrap 与 meboot 包中实现的最大熵 Bootstrap 进行了比较。结果表明，根据区间得分（Interval Score），DPQBootstrap 以 106 胜对 86 胜优于 meboot，显示出其保留原始时间序列统计特性的强大能力。此外，在贝叶斯 ARMA 预测框架内进行了一项实证应用，分别在有和没有 DPQBootstrap 集成的情况下进行。结果表明，纳入 DPQBootstrap 可以提高点预测精度和预测区间的质量，尤其是在与适当指定的 ARMA 模型结合使用时。
英文摘要
This work presents DPQBootstrap, a probabilistic resampling method for time series based on a Dirichlet quantile bootstrap and rank-based temporal reconstruction. The method aims to generate pseudo-series that preserve the main characteristics of the original series, including variability, quantiles, extreme values, and part of its temporal dependence. A simulation study compares DPQBootstrap with the Maximum Entropy Bootstrap implemented in the meboot package across eight simulation scenarios. The results show that DPQBootstrap achieves 106 wins over 86 for meboot according to the Interval Score, indicating a strong ability to preserve the statistical properties of the original time series. In addition, an empirical application was conducted within a Bayesian ARMA forecasting framework, with and without DPQBootstrap integration. The results indicate that incorporating DPQBootstrap can improve point forecast accuracy and the quality of predictive intervals, particularly when combined with appropriately specified ARMA models.
