This article presents a selective survey of algorithms for the offline detection of multiple
change points in multivariate time series. A general yet structuring methodological strategy …

Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind or
storms have devastating effects each year. One of the key challenges for society is …

Change point detection is an important part of time series analysis, as the presence of a
change point indicates an abrupt and significant change in the data generating process …

Online detection of changes in stochastic systems, referred to as sequential change
detection or quickest change detection, is an important research topic in statistics, signal …

Change points are a very common feature of 'big data'that arrive in the form of a data stream.
We study high dimensional time series in which, at certain time points, the mean structure …

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 …

We introduce a new estimator, the simultaneous multiscale change point estimator SMUCE,
for the change point problem in exponential family regression. An unknown step function is …

Research Methodology is meant to provide a broad guideline to facilitate and steer the
whole of a research activity in any discipline. With the ambit and amount of research …

Cryptocurrency has recently attracted substantial interest from investors due to its underlying
philosophy of decentralization and transparency. Considering cryptocurrency's volatility and …

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 …