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

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 …

For stationary time series, the cross-covariance and the cross-correlation as functions of time
lag n serve to quantify the similarity of two time series. The latter measure is also used to …

The detrending moving average (DMA) algorithm is a widely used technique to quantify the
long-term correlations of nonstationary time series and the long-range correlations of fractal …

The most suitable paradigms and tools for investigating the scaling structure of financial time
series are reviewed and discussed in the light of some recent empirical results. Different …

Noisy signals in many real-world systems display long-range autocorrelations and long-
range cross-correlations. Due to periodic trends, these correlations are difficult to quantify …

We calculate the Hurst exponent H (t) of several time series by dynamical implementation of
a recently proposed scaling technique: the detrending moving average (DMA). In order to …

The application of methods drawn from nonlinear and stochastic dynamics to the analysis of
cardiovascular time series is reviewed, with particular reference to the identification of …

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 …

Detrended fluctuation analysis (DFA) and detrended moving average (DMA) are two scaling
analysis methods designed to quantify correlations in noisy nonstationary signals. We …