Data series generated by complex systems exhibit fluctuations on many time scales and/or
broad distributions of the values. In both equilibrium and non-equilibrium situations, the …

We develop a method for the multifractal characterization of nonstationary time series, which
is based on a generalization of the detrended fluctuation analysis (DFA). We relate our …

Detrended fluctuation analysis (DFA) is a scaling analysis method used to quantify long-
range power-law correlations in signals. Many physical and biological signals are “noisy,” …

Many neurons display significant patterning in their spike trains (eg oscillations, bursting),
and there is accumulating evidence that information is contained in these patterns. In many …

We examine several recently suggested methods for the detection of long-range correlations
in data series based on similar ideas as the well-established Detrended Fluctuation Analysis …

When investigating the dynamical properties of complex multiple-component physical and
physiological systems, it is often the case that the measurable system's output does not …

We study correlation properties of the magnitude and the sign of the increments in the time
intervals between successive heartbeats during light sleep, deep sleep, and rapid eye …

We introduce a new method, multifractal cross-correlation analysis based on statistical
moments (MFSMXA), to investigate the long-term cross-correlations and cross-multifractality …

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 …

Detrended fluctuation analysis (DFA) is an improved method of classical fluctuation analysis
for nonstationary signals where embedded polynomial trends mask the intrinsic correlation …