Monitoring random profiles over time is used to assess whether the system of interest, generating the profiles, is operating under desired conditions at any time-point. In practice, accurate detection of a change-point within a sequence of responses that exhibit a functional relationship with multiple explanatory variables is an important goal for effectively monitoring such profiles. We present a nonparametric method utilizing ensembles of regression trees and random forests to model the functional relationship along with associated Kolmogorov-Smirnov statistic to monitor profile behavior. Through a simulation study considering multiple factors, we demonstrate that our method offers strong performance and competitive detection capability when compared to existing methods.

翻译：随时间监控随机轮廓用于评估生成轮廓的目标系统在任意时间点是否在期望条件下运行。在实践中，准确检测在展现与多个解释变量存在函数关系的响应序列中的变点，是有效监控此类轮廓的一个重要目标。我们提出了一种非参数方法，利用回归树集成与随机森林来建模函数关系，并结合Kolmogorov-Smirnov统计量来监控轮廓行为。通过考虑多种因素的模拟研究，我们证明与现有方法相比，我们的方法提供了强大的性能和具有竞争力的检测能力。