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：本文提出通过状态空间方法提取潜在平稳因子，以此扩展时间序列回归树的信息集。该方法在两个维度上对时间序列回归树进行了泛化。首先，它能够处理存在测量误差、非平稳趋势、季节性和/或缺失观测等不规则性的预测变量。其次，它提供了一种透明的方式，用于将领域特定理论引入时间序列回归树。作为副产品，该技术为构建强大的集成模型奠定了基础。其实际应用性通过实证宏观金融的视角进行了研究。