  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.
