Multiscale complex network for analyzing experimental multivariate time series
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 seri...
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Published in | Europhysics letters Vol. 109; no. 3; pp. 30005 - p1-30005-p6 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Les Ulis
EDP Sciences, IOP Publishing and Società Italiana di Fisica
01.02.2015
IOP Publishing |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | istex:C6C47A509E029E7143C8C3C0DC05A17A8F88E114 ark:/67375/80W-ZWZ5H6X7-W publisher-ID:epl16895 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0295-5075 1286-4854 |
DOI: | 10.1209/0295-5075/109/30005 |