| Issue |
EPL
Volume 118, Number 2, April 2017
|
|
|---|---|---|
| Article Number | 20003 | |
| Number of page(s) | 6 | |
| Section | General | |
| DOI | https://doi.org/10.1209/0295-5075/118/20003 | |
| Published online | 21 June 2017 | |
From fuzzy recurrence plots to scalable recurrence networks of time series
Department of Biomedical Engineering, Linkoping University - 581 83 Linkoping, Sweden
Received: 4 April 2017
Accepted: 31 May 2017
Recurrence networks, which are derived from recurrence plots of nonlinear time series, enable the extraction of hidden features of complex dynamical systems. Because fuzzy recurrence plots are represented as grayscale images, this paper presents a variety of texture features that can be extracted from fuzzy recurrence plots. Based on the notion of fuzzy recurrence plots, defuzzified, undirected, and unweighted recurrence networks are introduced. Network measures can be computed for defuzzified recurrence networks that are scalable to meet the demand for the network-based analysis of big data.
PACS: 05.45.Tp – Time series analysis / 07.05.Mh – Neural networks, fuzzy logic, artificial intelligence / 07.05.Rm – Data presentation and visualization: algorithms and implementation
© EPLA, 2017
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