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Change point detection is a commonly used technique in time series analysis,capturing the dynamic nature in which many real-world processes function. Withthe ever increasing troves of multivariate high-dimensional time series data,especially in neuroimaging and finance, there is a clear need for scalable anddata-driven change point detection methods. Currently, change point detectionmethods for multivariate high-dimensional data are scarce, with even lessavailable in high-level, easily accessible software packages. To this end, weintroduce the R package fabisearch, available on the Comprehensive R ArchiveNetwork (CRAN), which implements the factorized binary search (FaBiSearch)methodology. FaBiSearch is a novel statistical method for detecting changepoints in the network structure of multivariate high-dimensional time serieswhich employs non-negative matrix factorization (NMF), an unsuperviseddimension reduction and clustering technique. Given the high computational costof NMF, we implement the method in C++ code and use parallelization to reducecomputation time. Further, we also utilize a new binary search algorithm toefficiently identify multiple change points and provide a new method fornetwork estimation for data between change points. We show the functionality ofthe package and the practicality of the method by applying it to a neuroimagingand a finance data set. Lastly, we provide an interactive, 3-dimensional,brain-specific network visualization capability in a flexible, stand-alonefunction. This function can be conveniently used with any node coordinateatlas, and nodes can be color coded according to community membership (ifapplicable). The output is an elegantly displayed network laid over a corticalsurface, which can be rotated in the 3-dimensional space.

fabisearch: A Package for Change Point Detection in and Visualization of the Network Structure of Multivariate High-Dimensional Time Series in R