MAR_Variance

The function <code>MAR_Variance</code> is used for generating MAR model(s) for
examples of the functions <code>SNSeg_Uni</code>, <code>SNSeg_Multi</code>, and <code>SNSeg_HD</code>.

Implementations self-normalization (SN) based algorithms for
change-points estimation in time series data. This comprises nested
local-window algorithms for detecting changes in both univariate and
multivariate time series developed in Zhao, Jiang and Shao (2022)
<doi:10.1111/rssb.12552>.

Zifeng Zhao

SNSeg

Self-Normalization(SN) Based Change-Point Estimation for Time
Series

Shubo Sun

Feiyu Jiang

Xiaofeng Shao

MAR_Variance function

<dl><dt>reptime</dt>
<dd>The number of time series to be generated</dd>
<dt>type</dt>
<dd>The type of time series for simulation, which includes V1, V2, V3
, A1, A2 and A3. The V-beginnings are for testing the variance, and the
A-beginnings are for testing the autocorrelation. The simulated time series
come from supplement of Zhao et al. (2022) <a href="/link/doi%3A10.1111%2Frssb.12552?package=SNSeg&version=1.0.2" data-mini-rdoc="SNSeg::doi:10.1111/rssb.12552">doi:10.1111/rssb.12552</a>.
Default <code>type</code> is <code>V3</code>.
The time length and "true change-points locations" (cps) for each <code>type</code> are as follows:
<code>V1</code>: cps at 400 and 750 with a time length of 1024.
<code>V2</code>: cps at 125, 532 and 704 with a time length of 1024.
<code>V3</code>: cps at 512 and 768 with a time length of 1024.
<code>A1</code>: cps at 400 and 750 with a time length of 1024.
<code>A2</code>: cps at 50 with a time length of 1024.
<code>A3</code>: cps at 512 and 768 with a time length of 1024.</dd></dl>

Arguments

A funtion to generate a multivariate autoregressive process (MAR) model in
time series for testing change points based on variance and
autocovariance — MAR_Variance

<dl>

<dt>reptime</dt>
<dd>The number of time series to be generated</dd>


<dt>type</dt>
<dd>The type of time series for simulation, which includes V1, V2, V3
, A1, A2 and A3. The V-beginnings are for testing the variance, and the
A-beginnings are for testing the autocorrelation. The simulated time series
come from supplement of Zhao et al. (2022) <a href='doi:10.1111/rssb.12552'>doi:10.1111/rssb.12552</a>.
Default <code>type</code> is <code>V3</code>.
The time length and "true change-points locations" (cps) for each <code>type</code> are as follows:
<code>V1</code>: cps at 400 and 750 with a time length of 1024.
<code>V2</code>: cps at 125, 532 and 704 with a time length of 1024.
<code>V3</code>: cps at 512 and 768 with a time length of 1024.
<code>A1</code>: cps at 400 and 750 with a time length of 1024.
<code>A2</code>: cps at 50 with a time length of 1024.
<code>A3</code>: cps at 512 and 768 with a time length of 1024.</dd>

</dl>

MAR_Variance: A funtion to generate a multivariate autoregressive process (MAR) model in time series for testing change points based on variance and autocovariance

Description

Usage

Value

Arguments

Examples