Random Forests for Change Point Detection

We propose a novel multivariate nonparametric multiple change point detection method using classifiers. We construct a classifier log-likelihood ratio that uses class probability predictions to compare different change point configurations. We propose a computationally feasible search method that is particularly well suited for random forests, denoted by changeforest. However, the method can be paired with any classifier that yields class probability predictions, which we illustrate by also using a $k$-nearest neighbor classifier. We prove that it consistently locates change points …

change classifier classifiers construct detection likelihood multiple multivariate novel predictions probability random random forests search