2021
DOI: 10.48550/arxiv.2104.10470
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MFDFA: Efficient Multifractal Detrended Fluctuation Analysis in Python

Leonardo Rydin Gorjão,
Galib Hassan,
Jürgen Kurths
et al.
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Cited by 2 publications
(3 citation statements)
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“…3a the variance of the increment statistics increases in a power-law relation with the incremental lag τ . In order to best describe this-and to retrieve the Hurst exponent H-we employ Detrended Fluctuation Analysis (DFA) of the power-grid frequency recordings [51][52][53]. Recall that DFA studies the scaling of the fluctuations of a time series by studying the local properties of the data, similarly to what increment statistics does.…”
Section: E Amplitude Synchronisationmentioning
confidence: 99%
“…3a the variance of the increment statistics increases in a power-law relation with the incremental lag τ . In order to best describe this-and to retrieve the Hurst exponent H-we employ Detrended Fluctuation Analysis (DFA) of the power-grid frequency recordings [51][52][53]. Recall that DFA studies the scaling of the fluctuations of a time series by studying the local properties of the data, similarly to what increment statistics does.…”
Section: E Amplitude Synchronisationmentioning
confidence: 99%
“…Multifractal detrended fluctuation analysis (MFDFA) [49,62,63] is one of the principal methods to estimate the scaling function ξ(n) of one-dimensional time series. It is well suited for time series with trends, like price time series.…”
Section: Multifractal Detrended Fluctuation Analysismentioning
confidence: 99%
“…To obtain some statistics of the range of Hurst exponents using MFDFA, we estimate the Hurst exponent by fitting the ξ(2) function for various ranges of the snippets within the daily and hourly scale. In this way, the results are presented in a box-and-whiskers plot to best quantify the estimation of the Hurst exponents H [63].…”
Section: E Implementationmentioning
confidence: 99%