This paper gives an account of some of the recent work on structural breaks in time series
models. In particular, we show how procedures based on the popular cumulative sum …

Long-memory, or more generally fractal, processes are known to play an important role in
many scientific disciplines and applied fields such as physics, geophysics, hydrology …

Several tests for detecting mean shifts at an unknown time in stationary time series have
been proposed, including cumulative sum (CUSUM), Gaussian likelihood ratio (LR) …

This article considers the CUSUM-based (cumulative sum) test for a change point in a time
series. In the case of testing for a mean shift, the traditional Kolmogorov–Smirnov test …

The paper studies the change-point problem and the cross-covariance function for ARCH
models. Bounds for the cross-covariance function are derived and explicit formulae are …

Change point detection (CPD) aims to locate abrupt property changes in time series data.
Recent CPD methods demonstrated the potential of using deep learning techniques, but …

A common goal in modeling and data mining is to determine, based on sample data,
whether or not a change of some sort has occurred in a quantity of interest. The study of …

The primary contributions of this article are rigorously developed novel statistical methods
for detecting change points in multivariate time series. We extend the class of score type …

The assumption of (weak) stationarity is crucial for the validity of most of the conventional
tests of structure change in time series. Under complicated nonstationary temporal …

Autoregressive time series models of order p have p+ 2 parameters, the mean, the variance
of the white noise and the p autoregressive parameters. Change in any of these over time is …