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• W1913650225 startingPage "103105" @default.
• W1913650225 abstract "In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series." @default.
• W1913650225 created "2016-06-24" @default.
• W1913650225 creator A5047921674 @default.
• W1913650225 date "2015-10-01" @default.
• W1913650225 modified "2023-10-17" @default.
• W1913650225 title "Complex network approach to fractional time series" @default.
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• W1913650225 doi "https://doi.org/10.1063/1.4930839" @default.
• W1913650225 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26520071" @default.
• W1913650225 hasPublicationYear "2015" @default.
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