Journal Article

FZJ-2022-03340

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png MFDFA: Efficient multifractal detrended fluctuation analysis in python

Rydin Gorjão, L. (Corresponding author) ; Hassan, G. ; Kurths, J. ; Witthaut, D.FZJ*

2022
North Holland Publ. Co. Amsterdam

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Please use a persistent id in citations: http://hdl.handle.net/2128/31845  doi:10.1016/j.cpc.2021.108254

Abstract: 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.

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Contributing Institute(s):

1. Systemforschung und Technologische Entwicklung (IEK-STE)

Research Program(s):

1. 1112 - Societally Feasible Transformation Pathways (POF4-111) (POF4-111)
2. HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) (HDS-LEE-20190612)
3. ES2050 - Energie System 2050 (ES2050) (ES2050)

Appears in the scientific report 2022

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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