用自回归筛法重做随机森林抽样，提升时间序列预测精度
AR-Sieve Bootstrap for the Random Forest and a simulation-based comparison with rangerts time series prediction
- 用AR-Sieve替代传统抽样，更贴合时间序列的生成过程
- 模拟显示新方法在多种数据下预测误差更低
- 适合处理带依赖结构的时间序列数据的科研与工程人员
随机森林（RF）可广泛应用于各类问题，包括时间序列预测。然而，经典独立同分布（IID）Bootstrap和块抽样策略（如rangerts中实现）在重采样时未能充分反映数据生成过程（DGP）的特性。本文提出将随机森林与残差抽样结合，以自回归筛法（AR-Sieve Bootstrap, ARSB）取代IID Bootstrap，假设DGP为自回归过程。通过基于不同类型DGP生成的合成数据进行模拟研究，结果表明：使用ARSB可增加森林中各树之间的差异性；同时，相较于其他抽样策略，采用ARSB的随机森林在预测精度上表现更优。但这些改进也伴随一定的效率损失。
原文摘要 · Abstract (English)
The Random Forest (RF) algorithm can be applied to a broad spectrum of problems, including time series prediction. However, neither the classical IID (Independent and Identically distributed) bootstrap nor block bootstrapping strategies (as implemented in rangerts) completely account for the nature of the Data Generating Process (DGP) while resampling the observations. We propose the combination of RF with a residual bootstrapping technique where we replace the IID bootstrap with the AR-Sieve Bootstrap (ARSB), which assumes the DGP to be an autoregressive process. To assess the new model's predictive performance, we conduct a simulation study using synthetic data generated from different types of DGPs. It turns out that ARSB provides more variation amongst the trees in the forest. Moreover, RF with ARSB shows greater accuracy compared to RF with other bootstrap strategies. However, these improvements are achieved at some efficiency costs.
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