A Frequentist Approach to Change Point Detection: Methods and Applications

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.