fabisearch: A Package for Change Point Detection in and Visualization of the Network Structure of Multivariate High-Dimensional Time Series in R
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fabisearch：高维多元时间序列网络结构变点检测与可视化 R 包
- 研究问题
- 面对神经影像与金融等高维多元时间序列，如何高效检测变量间网络结构的多个变点，并估计和展示分段网络？
- 方法与思路
- 作者以非负矩阵分解实现 FaBiSearch，结合二分搜索、C++ 与并行计算定位变点，并提供分段网络估计及三维脑网络显示。
- 材料陈述的贡献
- 摘要介绍实现上述方法的 R 包，并以神经影像和金融数据展示功能与实用性，同时提供可独立使用的脑网络可视化函数。
- 阅读关注点
- 应核对网络结构及变点的统计定义、矩阵分解和搜索的调节参数、误检控制，以及计算成本和应用中的结果解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未获验证；摘要未说明检测的一致性条件、误差指标或软件当前的维护状态。
原文摘要
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.