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Surrogate-assisted network analysis of nonlinear time series

Laut, Ingo and Räth, Christoph (2016) Surrogate-assisted network analysis of nonlinear time series. STATPHYS26, 18.­‐22. Juli 2016, Lyon, Frankreich. (Unpublished)

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Abstract

A recent milestone in the feld of statistical physics has been complex network theory [1]. The constituents of complex systems are translated into the nodes of a network and their interactions are represented as edges. While this procedure is straightforward for systems like social or neural networks, there is no natural way of how to create a network from a time series. In this contribution, we compare recurrence networks [2] and symbolic networks [3] to the nonlinear prediction error concerning their performance in detecting nonlinearities in time series [4]. The tests are based on surrogate data sets, uncovering the disparity of the different surrogate generating algorithms. For synthetic data of the Lorenz system, the network measures show a comparable performance. In the case of relatively short and noisy real-world data from active galactic nuclei, the nonlinear prediction error yields more robust results than the network measures. In addition, we examine the correlations in the Fourier phases of data sets from some surrogate generating algorithms. The phase correlations tend to (anti)correlate with the measures of nonlinearity and can thus be held responsible for the weak performance of the algorithms in question. These findings may further increase the knowledge of the role the Fourier phases in the field of time series analysis [5]. [1] R. Albert and A.-L. Barabasi, Rev. Mod. Phys. 74, 47 (2002) [2] R. V. Donner, Y. Zou, J. F. Donges, N. Marwan, and J. Kurths, New J. Phys. 12, 033025 (2010) [3] X. Sun, M. Small, Y. Zhao, and X. Xue, Chaos 24, 024402 (2014) [4] I. Laut and C. Räth, submitted to Phys. Rev. E. (2016) [5] C. Räath and I. Laut, Phys. Rev. E 92, 040902(R) (2015)

Item URL in elib:https://elib.dlr.de/106146/
Document Type:Conference or Workshop Item (Speech)
Title:Surrogate-assisted network analysis of nonlinear time series
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Laut, IngoDLR Research Group Complex Plasma, OberpfaffenhofenUNSPECIFIED
Räth, ChristophDLR Research Group Complex Plasma, OberpfaffenhofenUNSPECIFIED
Date:2016
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Unpublished
Keywords:time series analysis, networks
Event Title:STATPHYS26
Event Location:Lyon, Frankreich
Event Type:international Conference
Event Dates:18.­‐22. Juli 2016
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Research under Space Conditions
DLR - Research area:Raumfahrt
DLR - Program:R FR - Forschung unter Weltraumbedingungen
DLR - Research theme (Project):R - Komplexe Plasmen / Data analysis
Location: Oberpfaffenhofen
Institutes and Institutions:Research Group Complex Plasma > Research Group Complex Plasma
Deposited By: Laut, Ingo
Deposited On:23 Sep 2016 09:58
Last Modified:23 Sep 2016 09:58

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