A Frequentist Approach to Change Point Detection: Methods and Applications
arXiv:2607.13852
summary
The paper proposes a frequentist method for detecting change points in functional time series, handling both sparse and dense observation designs and addressing shifts in mean and volatility, with a consistency proof for the estimator.
Abstract
In this paper we study the problem of change point detection in functional time series where the observations are allowed to vary on both sparse and dense support. We address the problem of mean shift as well as the process volatility. Our methodology is based on the maximization of the conditional probability of change point given all the other parameters. Further, it has been proved that the proposed estimator is consistent.
Topics & keywords
#change point detection#functional data analysis#time series#mean shift#volatility#frequentist methodsconditional probabilityconsistent estimatorfunctional time seriessparse supportdense support