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000909687 1001_ $$0P:(DE-Juel1)173608$$aRydin Gorjão, Leonardo$$b0$$eCorresponding author
000909687 245__ $$aMFDFA: Efficient multifractal detrended fluctuation analysis in python
000909687 260__ $$aAmsterdam$$bNorth Holland Publ. Co.$$c2022
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000909687 520__ $$aMultifractal detrended fluctuation analysis (MFDFA) has become a central method to characterise the variability and uncertainty in empiric time series. Extracting the fluctuations on different temporal scales allows quantifying the strength and correlations in the underlying stochastic properties, their scaling behaviour, as well as the level of fractality. Several extensions to the fundamental method have been developed over the years, vastly enhancing the applicability of MFDFA, e.g. empirical mode decomposition for the study of long-range correlations and persistence. In this article we introduce an efficient, easy-to-use python library for MFDFA, incorporating the most common extensions and harnessing the most of multi-threaded processing for very fast calculations.
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000909687 7001_ $$0P:(DE-HGF)0$$aHassan, Galib$$b1
000909687 7001_ $$0P:(DE-HGF)0$$aKurths, Jürgen$$b2
000909687 7001_ $$0P:(DE-Juel1)162277$$aWitthaut, Dirk$$b3$$ufzj
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