Estimation of multifractality based on natural time analysis

A. Mintzelas; N. V. Sarlis; S. R.G. Christopoulos

doi:10.1016/j.physa.2018.08.015

Estimation of multifractality based on natural time analysis

A. Mintzelas, N. V. Sarlis, S. R.G. Christopoulos

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

Abstract

Recent studies have shown that results deduced on the basis of a new time domain termed natural time reveal that novel dynamical features hidden behind time-series in complex systems can be uncovered. Here, we propose a method for estimating the multifractal behavior of time series by studying the fluctuations of natural time under time reversal. Examples of the application of this method to fractional Gaussian noises, fractional Brownian motions, binomial multifractal series, Lévy processes as well as interbeat intervals’ time series from electrocardiograms are presented.

Original language	English
Pages (from-to)	153-164
Number of pages	12
Journal	Physica A: Statistical Mechanics and its Applications
Volume	512
Early online date	13 Aug 2018
DOIs	https://doi.org/10.1016/j.physa.2018.08.015
Publication status	Published - 15 Dec 2018
Externally published	Yes

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title = "Estimation of multifractality based on natural time analysis",

abstract = "Recent studies have shown that results deduced on the basis of a new time domain termed natural time reveal that novel dynamical features hidden behind time-series in complex systems can be uncovered. Here, we propose a method for estimating the multifractal behavior of time series by studying the fluctuations of natural time under time reversal. Examples of the application of this method to fractional Gaussian noises, fractional Brownian motions, binomial multifractal series, L{\'e}vy processes as well as interbeat intervals{\textquoteright} time series from electrocardiograms are presented.",

keywords = "fBm, fGn, Heart rate variability, Multifractals, Natural time, Time reversal",

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AU - Sarlis, N. V.

AU - Christopoulos, S. R.G.

PY - 2018/12/15

Y1 - 2018/12/15

N2 - Recent studies have shown that results deduced on the basis of a new time domain termed natural time reveal that novel dynamical features hidden behind time-series in complex systems can be uncovered. Here, we propose a method for estimating the multifractal behavior of time series by studying the fluctuations of natural time under time reversal. Examples of the application of this method to fractional Gaussian noises, fractional Brownian motions, binomial multifractal series, Lévy processes as well as interbeat intervals’ time series from electrocardiograms are presented.

AB - Recent studies have shown that results deduced on the basis of a new time domain termed natural time reveal that novel dynamical features hidden behind time-series in complex systems can be uncovered. Here, we propose a method for estimating the multifractal behavior of time series by studying the fluctuations of natural time under time reversal. Examples of the application of this method to fractional Gaussian noises, fractional Brownian motions, binomial multifractal series, Lévy processes as well as interbeat intervals’ time series from electrocardiograms are presented.

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KW - fGn

KW - Heart rate variability

KW - Multifractals

KW - Natural time

KW - Time reversal

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JO - Physica A: Statistical Mechanics and its Applications

JF - Physica A: Statistical Mechanics and its Applications

SN - 0378-4371

ER -

Estimation of multifractality based on natural time analysis

Abstract

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