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A KST framework for Correlation Network Construction from Time series signals

机译：时间序列信号相关网络建设的KST框架

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A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.
机译：KST（KOLMOGOROV-SMIRNOV测试和T统计）方法用于基于多变量时间信号中的每个时间序列的波动构造相关网络。在该方法中，每个时间序列均相分为多个段，并且通过千次变化检测过程计算每个段中的最大数据波动。每个时间序列之间的连接源自数据波动矩阵，并且用于构造波动相关网络（FCN）。用合成模拟测试该方法，并将结果与​​使用KS或T的结果进行比较，仅用于检测数据波动。本研究的新颖性是相关分析基于每个时间序列的每个段的数据波动而不是在原始时间信号上，这对于许多真实世界的应用以及对大规模时间信号的分析来说更为有意义在现有知识不确定的地方。

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