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首页> 外文会议>International conference on graphic and image processing >A KST framework for Correlation Network Construction from Time series signals
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A KST framework for Correlation Network Construction from Time series signals

机译：从时间序列信号构建关联网络的KST框架

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摘要

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：基于多元时间信号内每个时间序列的波动，使用KST（Kolmogorov-Smirnov检验和T统计量）方法来构建相关网络。在这种方法中，每个时间序列均等地划分为多个段，并且每个段中的最大数据波动通过KST变化检测过程来计算。每个时间序列之间的联系是从数据波动矩阵得出的，并用于波动相关网络（FCN）的构建。对该方法进行了综合仿真测试，并将结果与​​仅使用KS或T进行数据波动检测的结果进行了比较。这项研究的新颖之处在于，相关性分析是基于每个时间序列每个片段中的数据波动，而不是原始的时间信号，这对于许多实际应用和大规模时间信号的分析将更有意义。先验知识不确定的地方。

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