Change-point detection in functional time series: Applications to age-specific mortality and fertility

TL;DR

This paper introduces two novel methods for detecting change points in age-specific mortality and fertility functional time series, improving the understanding of demographic shifts and forecast accuracy.

Contribution

It proposes two new change-point detection techniques tailored for functional time series of demographic data, enhancing analysis precision.

Findings

Successfully identified change points in Australian demographic data

Improved forecast accuracy by selecting optimal training periods

Demonstrated effectiveness of methods on real-world data

Abstract

We consider determining change points in a time series of age-specific mortality and fertility curves observed over time. We propose two detection methods for identifying these change points. The first method uses a functional cumulative sum statistic to pinpoint the change point. The second method computes a univariate time series of integrated squared forecast errors after fitting a functional time-series model before applying a change-point detection method to the errors to determine the change point. Using Australian age-specific fertility and mortality data, we apply these methods to locate the change points and identify the optimal training period to achieve improved forecast accuracy.