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 …

A new mathematical framework is presented for producing maps and large-scale averages
of temperature changes from weather station thermometer data for the purposes of climate …

An essential guide on high dimensional multivariate time series including all the latest topics
from one of the leading experts in the field Following the highly successful and much lauded …

In this article, we consider the problem of detecting multiple changepoints in large datasets.
Our focus is on applications where the number of changepoints will increase as we collect …

One of the key challenges in changepoint analysis is the ability to detect multiple changes
within a given time series or sequence. The changepoint package has been developed to …

We propose a new technique, called wild binary segmentation (WBS), for consistent
estimation of the number and locations of multiple change-points in data. We assume that …

Change point analysis has applications in a wide variety of fields. The general problem
concerns the inference of a change in distribution for a set of time-ordered observations …

Time series segmentation, which is also known as multiple-change-point detection, is a well-
established problem. However, few solutions have been designed specifically for high …

Change-point detection is the problem of discovering time points at which properties of time-
series data change. This covers a broad range of real-world problems and has been actively …

In this study, a penalized maximal F test (PMFT) is proposed for detecting undocumented
mean shifts that are not accompanied by any sudden change in the linear trend of time …