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• W2528042856 abstract "The performance of recurrence networks and symbolic networks to detect weak nonlinearities in time series is compared to the nonlinear prediction error. For the 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. The tests are based on surrogate data sets. The correlations in the Fourier phases of data sets from some surrogate generating algorithms are also examined. The phase correlations are shown to have an impact on the performance of the tests for nonlinearity." @default.
• W2528042856 created "2016-10-14" @default.
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• W2528042856 date "2016-10-01" @default.
• W2528042856 modified "2023-10-16" @default.
• W2528042856 title "Surrogate-assisted network analysis of nonlinear time series" @default.
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• W2528042856 doi "https://doi.org/10.1063/1.4964646" @default.
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