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Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has confirmed the existence of persistent long-range correlations in healthy heart rate variability data. In this paper, we present the…

Medical Physics · Physics 2009-11-10 J. C. Echeverria , M. S. Woolfson , J. A. Crowe , B. R. Hayes-Gill , G. D. H. Croaker , H. Vyas

Rank-Ordered Multifractal Analysis (ROMA), a recently developed technique that combines the ideas of parametric rank ordering and one parameter scaling of monofractals, has the capabilities of deciphering the multifractal characteristics of…

Earth and Planetary Astrophysics · Physics 2014-11-20 Sunny W. Y. Tam , Tom Chang , Paul M. Kintner , Eric M. Klatt

Detrended fluctuation analysis (DFA) has been proposed as a robust technique to determine possible long-range correlations in power-law processes [1]. However, recent studies have reported the susceptibility of DFA to trends [2] which give…

Statistical Mechanics · Physics 2007-05-23 Radhakrishnan Nagarajan , Rajesh G. Kavasseri

For many complex systems the interaction of different scales is among the most interesting and challenging features. It seems not very successful to extract the physical properties in different scale regimes by the existing approaches, such…

Fluid Dynamics · Physics 2015-05-14 L. P. Wang , Y. X. Huang

A multifractal analysis (MFA) is performed on three-dimensional grayscale images associated with natural porous structures (soil samples). First, computed tomography (CT) scans are carried out on the samples to generate 3D grayscale images.…

Data Analysis, Statistics and Probability · Physics 2016-05-22 Lorenzo Milazzo , Radoslaw Pajor

Multifractal formalisms provide an apt framework to study random cascades in which multifractal spectrum width $\Delta\alpha$ fluctuates depending on the number of estimable power-law relationships. Then again, multifractality without…

Adaptation and Self-Organizing Systems · Physics 2023-12-12 Madhur Mangalam , Aaron D Likens , Damian G Kelty-Stephen

In this paper, we present an assortment of both standard and advanced Fourier techniques that are useful in the analysis of astrophysical time series of very long duration -- where the observation time is much greater than the time…

Astrophysics · Physics 2009-11-07 Scott M. Ransom , Stephen S. Eikenberry , John Middleditch

Erraticity analysis of multiparticle production data is introduced as a way of extracting the maximum amount of information on self-similar fluctuations. It is presented as the next logical step to take beyond the intermittency analysis. An…

High Energy Physics - Phenomenology · Physics 2007-05-23 Rudolph C. Hwa

Recent evidence suggests that physiological signals under healthy conditions may have a fractal temporal structure. We investigate the possibility that time series generated by certain physiological control systems may be members of a…

Improvement in time resolution sometimes introduces short-range random noises into temporal data sequences. These noises affect the results of power-spectrum analyses and the Detrended Fluctuation Analysis (DFA). The DFA is one of useful…

Data Analysis, Statistics and Probability · Physics 2009-02-05 Shin-ichi Tadaki

In this letter we have analyzed the temporal correlations of the angle-of-arrival fluctuations of stellar images. Experimentally measured data were carefully examined by implementing multifractal detrended fluctuation analysis. This…

Atmospheric and Oceanic Physics · Physics 2023-07-19 Luciano Zunino , Damián Gulich , Gustavo Funes , Aziz Ziad

We consider the set of monofractals within a multifractal related to the phase space being the support of a generalized thermostatistics. The statistical weight exponent $\tau(q)$ is shown to can be modeled by the hyperbolic tangent…

Statistical Mechanics · Physics 2007-05-23 A. I. Olemskoi , V. O. Kharchenko

We discuss the problem for detecting long-range correlations in sequences of values obtained by generators of pseudo-random numbers. The basic idea is that the H{\"o}lder exponent for a sufficiently long sequence of uncorrelated random…

Data Analysis, Statistics and Probability · Physics 2007-05-23 Nikolay K. Vitanov , Kh. Tarnev , H. Kantz

We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analysing the multi/uni-scaling behaviour of…

Statistical Finance · Quantitative Finance 2015-09-22 Riccardo Junior Buonocore , Tomaso Aste , Tiziana Di Matteo

Dimension reduction techniques for multivariate time series decompose the observed series into a few useful independent/orthogonal univariate components. We develop a spectral domain method for multivariate second-order stationary time…

Methodology · Statistics 2020-10-12 Raanju R. Sundararajan

Over the past three decades, describing the reality surrounding us using the language of complex networks has become very useful and therefore popular. One of the most important features, especially of real networks, is their complexity,…

Physics and Society · Physics 2024-10-16 Rafal Rak , Ewa Rak

We study temporal correlations and multifractal properties of long river discharge records from 41 hydrological stations around the globe. To detect long-term correlations and multifractal behaviour in the presence of trends, we apply…

Detailed study of multifractal characteristics of the financial time series of asset values and of its returns is performed using a collection of the high frequency Deutsche Aktienindex data. The tail index ($\alpha$), the Renyi exponents…

Statistical Mechanics · Physics 2009-11-07 A. Z. Gorski , S. Drozdz , J. Speth

Fractal dimensions of eigenfunctions for various critical random matrix ensembles are investigated in perturbation series in the regimes of strong and weak multifractality. In both regimes we obtain expressions similar to those of the…

Chaotic Dynamics · Physics 2011-09-26 E. Bogomolny , O. Giraud

Multivariate time-series forecasting holds immense value across diverse applications, requiring methods to effectively capture complex temporal and inter-variable dynamics. A key challenge lies in uncovering the intrinsic patterns that…

Machine Learning · Computer Science 2025-03-12 Liang Yu , Lai Tu , Xiang Bai
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