MFDFA：Python中高效的多重分形去趋势波动分析
MFDFA: Efficient Multifractal Detrended Fluctuation Analysis in Python
摘要：
多重分形去趋势波动分析（MFDFA）已成为研究经验时间序列的变异性和不确定性的重要方法。提取不同时间尺度上的波动允许量化的强度和相关性的基本随机属性，其缩放行为，以及分形的水平。几个扩展的基本方法已经开发了多年来，大大提高了MFDFA的适用性，例如经验模式分解的长期相关性和持久性的研究。在本文中，我们将为MFDFA介绍一个高效、易于使用的Python库，它包含了最常见的扩展，并利用大多数多线程处理来实现非常快速的计算。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
