This manuscript proposes to extend the information set of time-series regression trees with latent stationary factors extracted via state-space methods. In doing so, this approach generalises time-series regression trees on two dimensions. First, it allows to handle predictors that exhibit measurement error, non-stationary trends, seasonality and/or irregularities such as missing observations. Second, it gives a transparent way for using domain-specific theory to inform time-series regression trees. As a byproduct, this technique sets the foundations for structuring powerful ensembles. Their real-world applicability is studied under the lenses of empirical macro-finance.

翻译：本手稿建议扩大时间序列回归树的信息系列,并采用通过国家空间方法提取的潜在固定因素。 在这样做时,这种方法在两个层面概括了时间序列回归树。 首先,它能够处理显示测量错误、非静止趋势、季节性和/或缺失观察等异常现象的预测者。 其次,它提供了一种透明的方式,用于使用特定领域的理论来为时间序列回归树提供信息。 作为一个副产品,这一技术为构建强大的组合打下了基础。 其实际适用性在经验宏观金融的视角下研究。