Change Point Detection in High-Dimensional Time Series Networks
// Implementation of the Factorized Binary Search (FaBiSearch)
methodology for the estimation of the number and the location
of multiple change points in the network (or clustering)
structure of multivariate high-dimensional time series. The
method is motivated by the detection of change points in
functional connectivity networks for functional magnetic
resonance imaging (fMRI) data. FaBiSearch uses non-negative
matrix factorization (NMF), an unsupervised dimension reduction
technique, and a new binary search algorithm to identify
multiple change points. It requires minimal assumptions.
Lastly, we provide interactive, 3-dimensional, brain-specific
network visualization capability in a flexible, stand-alone
function. This function can be conveniently used with any node
coordinate atlas, and nodes can be color coded according to
community membership, if applicable. The output is an elegantly
displayed network laid over a cortical surface, which can be
rotated in the 3-dimensional space. The main routines of the
package are detect.cps(), for multiple change point detection,
est.net(), for estimating a network between stationary
multivariate time series, net.3dplot(), for plotting the
estimated functional connectivity networks, and opt.rank(), for
finding the optimal rank in NMF for a given data set. The
functions have been extensively tested on simulated
multivariate high-dimensional time series data and fMRI data.
For details on the FaBiSearch methodology, please see Ondrus et
al. (2021) arXiv:2103.06347. For a more detailed explanation
and applied examples of the fabisearch package, please see
Ondrus and Cribben (2022), preprint.