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 …

Very long and noisy sequence data arise from biological sciences to social science
including high throughput data in genomics and stock prices in econometrics. Often such …

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 …

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 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 …

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 …

We propose a new, generic and flexible methodology for non-parametric function estimation,
in which we first estimate the number and locations of any features that may be present in …

A MOSUM procedure for the estimation of multiple random change points Page 1 Bernoulli
24(1), 2018, 526–564 DOI: 10.3150/16-BEJ887 A MOSUM procedure for the estimation of …

The task of root cause analysis (RCA) is to identify the root causes of system faults/failures
by analyzing system monitoring data. Efficient RCA can greatly accelerate system failure …