Multifractality is ubiquitously observed in complex natural and socioeconomic systems.
Multifractal analysis provides powerful tools to understand the complex nonlinear nature of …

This review addresses a central question in the field of complex systems: given a fluctuating
(in time or space), sequentially measured set of experimental data, how should one analyze …

Multifractal detrended fluctuation analysis (MFDFA) has become a central method to
characterise the variability and uncertainty in empiric time series. Extracting the fluctuations …

In this paper, the multifractality degree in a collection of developed and emerging stock
market indices is evaluated. Empirical results suggest that the multifractality degree can be …

Abstract Based on the Multifractal Detrended Fluctuation Analysis (MFDFA) and on the
Wavelet Transform Modulus Maxima (WTMM) methods we investigate the origin of …

Background Chaos and random fractal theories are among the most important for fully
characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis …

Abstract We use Multi-Fractal Detrended Cross-Correlation Analysis (MF-DXA) method to
investigate the cross-correlation of temporal and spatial inter-events seismic data, which …

In this paper multifractal detrended fluctuation analysis (MFDFA) is used to study the human
gait time series for normal and diseased sets. It is observed that long range correlation is …

We use some fractal analysis methods to study river flow fluctuations. The result of the
Multifractal Detrended Fluctuation Analysis (MF-DFA) shows that there are two crossover …

Vibration data of faulty rolling bearings are usually nonstationary and nonlinear, and contain
fairly weak fault features. As a result, feature extraction of rolling bearing fault data is always …