random_Phi

Generates a \(p \times p\) autoregressive coefficient matrix
\(\Phi\) for the VAR(1) component in the proposed model. The diagonal
entries are randomly chosen from {0.5, -0.5}, and a specified number of
off-diagonal elements are randomly assigned nonzero values to introduce
cross-dependence among variables.

Implements methods for multiple change point detection in multivariate
time series with non-stationary dynamics and cross-correlations. The methodology
is based on a model in which each component has a fluctuating mean represented by
a random walk with occasional abrupt shifts, combined with a stationary vector
autoregressive structure to capture temporal and cross-sectional dependence. The
framework is broadly applicable to correlated multivariate sequences in which
large, sudden shifts occur in all or subsets of components and are the primary
targets of interest, whereas small, smooth fluctuations are not. Although random
walks are used as a modeling device, they provide a flexible approximation for a
wide class of slowly varying or locally smooth dynamics, enabling robust
performance beyond the strict random walk setting.

Yuhan Tian

FluxPoint

Change Point Detection for Non-Stationary and Cross-Correlated
Time Series

Abolfazl Safikhani

random_Phi function

<dl><dt>p</dt>
<dd>Integer. Dimension of the square matrix (\(p\) variables).</dd>
<dt>num_nonzero</dt>
<dd>Integer. Target number of nonzero off-diagonal
entries in \(\Phi\).</dd></dl>

Arguments

Randomly generate an autoregressive coefficient matrix \(\Phi\) — random_Phi

<dl>

<dt>p</dt>
<dd>Integer. Dimension of the square matrix (\(p\) variables).</dd>


<dt>num_nonzero</dt>
<dd>Integer. Target number of nonzero off-diagonal
entries in \(\Phi\).</dd>

</dl>

random_Phi: Randomly generate an autoregressive coefficient matrix \(\Phi\)

Description

Usage

Value

Arguments

Details