MFDFA: Efficient multifractal detrended fluctuation analysis in python

Rydin Gorjão, Leonardo; Hassan, Galib; Kurths, Jürgen; Witthaut, Dirk

doi:10.1016/j.cpc.2021.108254

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

学術論文

MFDFA: Efficient multifractal detrended fluctuation analysis in python

Authors

Rydin Gorjão, Leonardo
External Organizations;

Hassan, Galib
External Organizations;

/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

Witthaut, Dirk
External Organizations;

URL

There are no locators available

フルテキスト (公開)

There are no public fulltexts stored in PIKpublic

付随資料 (公開)

There is no public supplementary material available

引用

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

引用: https://publications.pik-potsdam.de/pubman/item/item_27024

要旨

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.