Description: MFDFA: Efficient multifractal detrended fluctuation analysis in python

This title appears in the Scientific Report : 2022

MFDFA: Efficient multifractal detrended fluctuation analysis in python

Multifractal 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, t...

Personal Name(s):	Rydin Gorjão, Leonardo (Corresponding author)
	Hassan, Galib / Kurths, Jürgen / Witthaut, Dirk
Contributing Institute:	Systemforschung und Technologische Entwicklung; IEK-STE
Published in:	Computer physics communications, 273 (2022) S. 108254 -
Imprint:	Amsterdam North Holland Publ. Co. 2022
DOI:	10.1016/j.cpc.2021.108254
Document Type:	Journal Article
Research Program:	Energie System 2050 Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) Societally Feasible Transformation Pathways
Link:	OpenAccess
	Publikationsportal JuSER

Please use the identifier: http://hdl.handle.net/2128/31845 in citations.
Please use the identifier: http://dx.doi.org/10.1016/j.cpc.2021.108254 in citations.

Multifractal 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.