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@ARTICLE{RydinGorjo:909687,
      author       = {Rydin Gorjão, Leonardo and Hassan, Galib and Kurths,
                      Jürgen and Witthaut, Dirk},
      title        = {{MFDFA}: {E}fficient multifractal detrended fluctuation
                      analysis in python},
      journal      = {Computer physics communications},
      volume       = {273},
      issn         = {0010-4655},
      address      = {Amsterdam},
      publisher    = {North Holland Publ. Co.},
      reportid     = {FZJ-2022-03340},
      pages        = {108254 -},
      year         = {2022},
      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.},
      cin          = {IEK-STE},
      ddc          = {530},
      cid          = {I:(DE-Juel1)IEK-STE-20101013},
      pnm          = {1112 - Societally Feasible Transformation Pathways
                      (POF4-111) / HDS LEE - Helmholtz School for Data Science in
                      Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612) / ES2050
                      - Energie System 2050 (ES2050)},
      pid          = {G:(DE-HGF)POF4-1112 / G:(DE-Juel1)HDS-LEE-20190612 /
                      G:(DE-HGF)ES2050},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000754669600006},
      doi          = {10.1016/j.cpc.2021.108254},
      url          = {https://juser.fz-juelich.de/record/909687},
}