inver

internal

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

inver function

<dl><dt>A</dt>
<dd>Numeric matrix.</dd></dl>

Arguments

Matrix inverse — inver

<dl>

<dt>A</dt>
<dd>Numeric matrix.</dd>

</dl>

inver: Matrix inverse

Description

Usage

Value

Arguments