10 April 2018 A KST framework for correlation network construction from time series signals
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 1061556 (2018) https://doi.org/10.1117/12.2303598
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
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.
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Jin-Peng Qi, Jin-Peng Qi, Quan Gu, Quan Gu, Ying Zhu, Ying Zhu, Ping Zhang, Ping Zhang, } "A KST framework for correlation network construction from time series signals", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061556 (10 April 2018); doi: 10.1117/12.2303598; https://doi.org/10.1117/12.2303598
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