Volume 109, Number 3, February 2015
|Number of page(s)||6|
|Published online||20 February 2015|
Multiscale complex network for analyzing experimental multivariate time series
1 School of Electrical Engineering and Automation, Tianjin University - Tianjin 300072, China
2 Department of Physics, East China Normal University - Shanghai 200241, China
3 Key Laboratory of Computer Vision and System (Ministry of Education) and Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology - Tianjin 300384, China
4 College of Electronic Information and Automation, Tianjin University of Science and Technology Tianjin 300222, China
Received: 16 December 2014
Accepted: 31 January 2015
The multiscale phenomenon widely exists in nonlinear complex systems. One efficient way to characterize complex systems is to measure time series and then extract information from the measurements. We propose a reliable method for constructing a multiscale complex network from multivariate time series. In particular, for a given multivariate time series, we first perform a coarse-grained operation to define temporal scales and then reconstruct the multivariate phase-space for each scale to infer multiscale complex networks. In addition, we develop a novel clustering coefficient entropy to assess the derived multiscale complex networks, aiming to characterize the coupled dynamical characteristics underlying multivariate time series. We apply our proposed approach to the analysis of multivariate time series measured from gas-liquid two-phase flow experiments. The results yield novel insights into the inherent coupled flow behavior underlying a realistic multiphase flow system. Bridging multiscale analysis and complex network provides a fascinating methodology for probing multiscale complex behavior underlying complex systems.
PACS: 05.45.Tp – Time series analysis / 47.55.Ca – Gas/liquid flows / 89.75.Fb – Structures and organization in complex systems
© EPLA, 2015
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