Change Point Analysis of Correlation in Non-stationary Time Series

01/30/2018
by   Holger Dette, et al.
0

A restrictive assumption in change point analysis is "stationarity under the null hypothesis of no change-point", which is crucial for asymptotic theory but not very realistic from a practical point of view. For example, if change point analysis for correlations is performed, it is not necessarily clear that the mean, marginal variance or higher order moments are constant, even if there is no change in the correlation. This paper develops change point analysis for the correlation structures under less restrictive assumptions. In contrast to previous work, our approach does not require that the mean, variance and fourth order joint cumulants are constant under the null hypothesis. Moreover, we also address the problem of detecting relevant change points.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/13/2022

Detecting A Single Change-point

This chapter overviews some of the work on detecting and estimating the ...
research
12/06/2021

Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams

Timeseries partitioning is an essential step in most machine-learning dr...
research
08/21/2021

Equivariant Variance Estimation for Multiple Change-point Model

The variance of noise plays an important role in many change-point detec...
research
01/19/2022

Using Joint Random Partition Models for Flexible Change Point Analysis in Multivariate Processes

Change point analyses are concerned with identifying positions of an ord...
research
04/29/2017

Learning with Changing Features

In this paper we study the setting where features are added or change in...
research
12/10/2021

Segmenting Time Series via Self-Normalization

We propose a novel and unified framework for change-point estimation in ...
research
05/28/2021

Epidemic change-point detection in general causal time series

We consider an epidemic change-point detection in a large class of causa...

Please sign up or login with your details

Forgot password? Click here to reset