On system behaviour using complex networks of a compression algorithm

We construct complex networks of scalar time series using a data compression algorithm. The structure and statistics of the resulting networks can be used to help characterize complex systems, and one property, in particular, appears to be a useful discriminating statistic in surrogate data hypothes...

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Bibliographic Details
Published in:Chaos (Woodbury, N.Y.) N.Y.), 2018-01, Vol.28 (1), p.013101-013101
Main Authors: Walker, David M., Correa, Debora C., Small, Michael
Format: Article
Language:English
Subjects:
Algorithms
Bifurcations
Chaos theory
Complex systems
Data compression
Networks
Time series
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Summary:We construct complex networks of scalar time series using a data compression algorithm. The structure and statistics of the resulting networks can be used to help characterize complex systems, and one property, in particular, appears to be a useful discriminating statistic in surrogate data hypothesis tests. We demonstrate these ideas on systems with known dynamical behaviour and also show that our approach is capable of identifying behavioural transitions within electroencephalogram recordings as well as changes due to a bifurcation parameter of a chaotic system. The technique we propose is dependent on a coarse grained quantization of the original time series and therefore provides potential for a spatial scale-dependent characterization of the data. Finally the method is as computationally efficient as the underlying compression algorithm and provides a compression of the salient features of long time series.
ISSN:1054-1500
1089-7682
DOI:10.1063/1.5001955