Change point detection is a commonly used technique in time series analysis, capturing the dynamic nature in which many real-world processes function. With the ever increasing troves of multivariate high-dimensional time series data, especially in neuroimaging and finance, there is a clear need for scalable and data-driven change point detection methods. Currently, change point detection methods for multivariate high-dimensional data are scarce, with even less available in high-level, easily accessible software packages. To this end, we introduce the R package fabisearch, available on the Comprehensive R Archive Network (CRAN), which implements the factorized binary search (FaBiSearch) methodology. FaBiSearch is a novel statistical method for detecting change points in the network structure of multivariate high-dimensional time series which employs non-negative matrix factorization (NMF), an unsupervised dimension reduction and clustering technique. Given the high computational cost of NMF, we implement the method in C++ code and use parallelization to reduce computation time. Further, we also utilize a new binary search algorithm to efficiently identify multiple change points and provide a new method for network estimation for data between change points. We show the functionality of the package and the practicality of the method by applying it to a neuroimaging and a finance data set. Lastly, we provide an 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.
翻译:在时间序列分析中,常见的一种常用技术是变点检测,它捕捉了许多真实世界进程运行的动态性质。随着多变量高维时间序列数据的日益剧烈,特别是在神经成形和融资方面,显然需要可缩放和数据驱动的变化点检测方法。目前,多变量高维数据的变化点检测方法非常稀少,在高层次、容易获取的软件包中甚至更少可用。为此,我们引入了在综合 R 存档网络( CRAAN) 上提供的R 软件包搜索,该软件在其中运行了因子化的双向双向双向搜索( FabiSearch) 应用了可应用的双向直观搜索( FabiSearch) 方法。FabiSearch是一种新颖的统计方法,用于探测多变量高维度高维度时间序列网络网络网络网络结构中的变化点,使用非负式矩阵化的尺寸降低和组合技术。鉴于NMFMF的计算成本很高,我们使用C+码方法来减少计算时间。此外,我们还可以使用新的二元搜索算算算法来有效识别多度的直径直径直径直径网络功能。我们用了一个数据功能来显示一个设定的功能。