transformData

Transforms the input time series data into the design matrices required for VAR estimation.
This includes centering, optional scaling, and constructing the lagged predictor matrix.

internal

Implements the algorithm introduced in Tian, Y., and Safikhani, A. (2024)
<doi:10.5705/ss.202024.0182>, "Sequential Change Point Detection in High-dimensional
Vector Auto-regressive Models". This package provides tools for detecting change points
in the transition matrices of VAR models, effectively identifying shifts in temporal
and cross-correlations within high-dimensional time series data.

Yuhan Tian

VARcpDetectOnline

Sequential Change Point Detection for High-Dimensional VAR
Models

Abolfazl Safikhani

transformData function

<dl><dt>data</dt>
<dd>A numeric matrix or data frame with time series data (observations in rows,
variables in columns).</dd>
<dt>p</dt>
<dd>Integer. The order of the VAR model (number of lags).</dd>
<dt>opt</dt>
<dd>List. Options for data transformation. Supported options include:<ul>
<li><code>scale</code>: Logical. Whether to scale the data columns (default is <code>FALSE</code>).</li>
<li><code>center</code>: Logical. Whether to center the data columns (default is <code>TRUE</code>).</li>
</ul></dd></dl>

Arguments

Transform Data for VAR Estimation — transformData

<dl>

<dt>data</dt>
<dd>A numeric matrix or data frame with time series data (observations in rows,
variables in columns).</dd>


<dt>p</dt>
<dd>Integer. The order of the VAR model (number of lags).</dd>


<dt>opt</dt>
<dd>List. Options for data transformation. Supported options include:<ul>
<li><code>scale</code>: Logical. Whether to scale the data columns (default is <code>FALSE</code>).</li>
<li><code>center</code>: Logical. Whether to center the data columns (default is <code>TRUE</code>).</li>
</ul></dd>

</dl>

transformData: Transform Data for VAR Estimation

Description

Usage

Value

Arguments