Universität zu Köln

Gorjao, Leonardo Rydin, Hassan, Galib, Kurths, Juergen and Witthaut, Dirk ORCID: 0000-0002-3623-5341 (2022). MFDFA: Efficient multifractal detrended fluctuation analysis in python. Comput. Phys. Commun., 273. AMSTERDAM: ELSEVIER. ISSN 1879-2944

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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. (C) 2021 Elsevier B.V. All rights reserved.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Gorjao, Leonardo Rydin UNSPECIFIED UNSPECIFIED UNSPECIFIED Hassan, Galib UNSPECIFIED UNSPECIFIED UNSPECIFIED Kurths, Juergen UNSPECIFIED UNSPECIFIED UNSPECIFIED Witthaut, Dirk UNSPECIFIED orcid.org/0000-0002-3623-5341 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-683615
DOI:	10.1016/j.cpc.2021.108254
Journal or Publication Title:	Comput. Phys. Commun.
Volume:	273
Date:	2022
Publisher:	ELSEVIER
Place of Publication:	AMSTERDAM
ISSN:	1879-2944
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language EMPIRICAL MODE DECOMPOSITION; TIME-SERIES; RAINFALL; MARKET Multiple languages Computer Science, Interdisciplinary Applications; Physics, Mathematical Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68361