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 …

Many common approaches to detecting changepoints, for example based on statistical
criteria such as penalised likelihood or minimum description length, can be formulated in …

Many traditional methods for identifying changepoints can struggle in the presence of
outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints to fit …

There exist plenty of outliers in power curve of wind turbines, which is not conducive to the
follow-up information mining. Improving the wind power curve data quality of wind turbines …

Recently, there have been an increasing number of studies using change-point methods to
detect artificial or natural discontinuities and regime shifts in climate. However, a major …

Many time series are characterised by abrupt changes in structure, such as sudden jumps in
level or volatility. We consider change points to be those time points which divide a data set …

The southern westerly winds (SWW) play a major role in climate variability in Southern
Hemisphere mid-and high-latitudes, regulating rainfall, ocean circulation, and the Southern …

This paper reviews recent developments in fundamental limits and optimal algorithms for
change point analysis. We focus on minimax optimal rates in change point detection and …

Spatially aggregated extreme rainfall over India shows an increasing trend over the last 50
years of the 20th century, while fine‐resolution analysis reveals a spatially nonuniform trend …