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  MFDFA: Efficient multifractal detrended fluctuation analysis in python

Rydin Gorjão, L., Hassan, G., Kurths, J., Witthaut, D. (2022): MFDFA: Efficient multifractal detrended fluctuation analysis in python. - Computer Physics Communications, 273, 108254.
https://doi.org/10.1016/j.cpc.2021.108254

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 Creators:
Rydin Gorjão, Leonardo1, Author
Hassan, Galib1, Author
Kurths, Jürgen2, Author              
Witthaut, Dirk1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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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.

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Language(s): eng - English
 Dates: 2021-12-032021-12-222022-04
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.cpc.2021.108254
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
Model / method: Open Source Software
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Computer Physics Communications
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 273 Sequence Number: 108254 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/201801311
Publisher: Elsevier