runCorr

Extracts the running correlations by sliding a window comprised of <code>wsize</code> time points, and in each window,
the correlation of each pair of variables is computed.
Each time the window is slid, the oldest time point is discarded and the latest time point is added.

The running statistics of interest is first extracted using a time window which is slid across the time series, and in each window, the running statistics value is computed. KCP (Kernel Change Point) detection proposed by Arlot et al. (2012) <arXiv:1202.3878> is then implemented to flag the change points on the running statistics (Cabrieto et al., 2018, <doi:10.1016/j.ins.2018.03.010>). Change points are located by minimizing a variance criterion based on the pairwise similarities between running statistics which are computed via the Gaussian kernel. KCP can locate change points for a given k number of change points. To determine the optimal k, the KCP permutation test is first carried out by comparing the variance of the running statistics extracted from the original data to that of permuted data. If this test is significant, then there is sufficient evidence for at least one change point in the data. Model selection is then used to determine the optimal k>0.

Kristof Meers

kcpRS

Kernel Change Point Detection on the Running Statistics

Jedelyn Cabrieto

Evelien Schat

Janne Adolf

Peter Kuppens

Francis Tuerlinckx

Eva Ceulemans

runCorr function

<dl><dt>data</dt>
<dd>N x v dataframe where N is the no. of time points and v the no. of variables</dd>
<dt>wsize</dt>
<dd>window size</dd></dl>

Arguments

Running Correlations — runCorr

<dl>

<dt>data</dt>
<dd>N x v dataframe where N is the no. of time points and v the no. of variables</dd>


<dt>wsize</dt>
<dd>window size</dd>

</dl>

runCorr: Running Correlations

Description

Usage

Value

Arguments

Examples