Nonparametric Multivariate Profile Monitoring Via Tree Ensembles

TL;DR

This paper introduces a nonparametric multivariate profile monitoring method using tree ensembles and Kolmogorov-Smirnov statistics, effectively detecting change-points in complex functional data.

Contribution

The paper proposes a novel nonparametric approach leveraging regression tree ensembles for multivariate profile monitoring, enhancing detection accuracy over existing methods.

Findings

Strong performance demonstrated in simulation studies

Competitive detection capability compared to existing methods

Effective monitoring of complex multivariate profiles

Abstract

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.