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A KST framework for correlation network construction from time series signals
Author(s): Jin-Peng Qi; Quan Gu; Ying Zhu; Ping Zhang
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Paper 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.

Paper Details

Date Published: 10 April 2018
PDF: 7 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061556 (10 April 2018); doi: 10.1117/12.2303598
Show Author Affiliations
Jin-Peng Qi, Donghua Univ. (China)
Quan Gu, Donghua Univ. (China)
Univ. of Glasgow (United Kingdom)
Ying Zhu, Royal North Shore Hospital (Australia)
Ping Zhang, Griffith Univ. (Australia)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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