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 …

We provide an overview and perspective on the Phase I collection and analysis of data for
use in process improvement and control charting. In Phase I, the focus is on understanding …

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 …

In this paper we build on an approach proposed by Zou et al.(2014) for nonparametric
changepoint detection. This approach defines the best segmentation for a data set as the …

We consider a general formulation of the multiple change-point problem, in which the data is
assumed to belong to a set equipped with a positive semidefinite kernel. We propose a …

Detecting the emergence of an abrupt change-point is a classic problem in statistics and
machine learning. Kernel-based nonparametric statistics have been proposed for this task …

In this paper we study the kernel change-point algorithm (KCP) proposed by Arlot, Celisse
and Harchaoui 5, which aims at locating an unknown number of change-points in the …

We propose a novel multivariate nonparametric multiple change point detection method
using classifiers. We construct a classifier log-likelihood ratio that uses class probability …

Data segmentation aka multiple change point analysis has received considerable attention
due to its importance in time series analysis and signal processing, with applications in a …

Due to the exponential growth of the Internet of Things networks and the massive amount of
time series data collected from these networks, it is essential to apply efficient methods for …