TY  - JOUR
AU  - Rydin Gorjão, Leonardo
AU  - Hassan, Galib
AU  - Kurths, Jürgen
AU  - Witthaut, Dirk
TI  - MFDFA: Efficient multifractal detrended fluctuation analysis in python
JO  - Computer physics communications
VL  - 273
SN  - 0010-4655
CY  - Amsterdam
PB  - North Holland Publ. Co.
M1  - FZJ-2022-03340
SP  - 108254 -
PY  - 2022
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000754669600006
DO  - DOI:10.1016/j.cpc.2021.108254
UR  - https://juser.fz-juelich.de/record/909687
ER  -