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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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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_27024 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_27024_1
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 ???ViewItemFull_lblCreators???:
Rydin Gorjão, Leonardo1, ???ENUM_CREATORROLE_AUTHOR???
Hassan, Galib1, ???ENUM_CREATORROLE_AUTHOR???
Kurths, Jürgen2, ???ENUM_CREATORROLE_AUTHOR???           
Witthaut, Dirk1, ???ENUM_CREATORROLE_AUTHOR???
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 ???ViewItemFull_lblAbstract???: 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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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2021-12-032021-12-222022-04
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1016/j.cpc.2021.108254
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???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Complex Networks
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Nonlinear Dynamics
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Machine Learning
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Open Source Software
???ENUM_IDENTIFIERTYPE_WORKINGGROUP???: Network- and machine-learning-based prediction of extreme events
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???ViewItemFull_lblSourceTitle???: Computer Physics Communications
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3
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???ViewItemFull_lblPages???: ???lbl_noEntry??? ???ViewItemFull_lblSourceVolumeIssue???: 273 ???ViewItemFull_lblSourceSequenceNo???: 108254 ???ViewItemFull_lblSourceStartEndPage???: ???lbl_noEntry??? ???ViewItemFull_lblSourceIdentifier???: ???ENUM_IDENTIFIERTYPE_CONE???: https://publications.pik-potsdam.de/cone/journals/resource/201801311
???ENUM_IDENTIFIERTYPE_PUBLISHER???: Elsevier