Results 41 to 50 of about 2,847 (165)

Nonlinear complexity analysis of brain FMRI signals in schizophrenia. [PDF]

open access: yesPLoS ONE, 2014
We investigated the differences in brain fMRI signal complexity in patients with schizophrenia while performing the Cyberball social exclusion task, using measures of Sample entropy and Hurst exponent (H).
Moses O Sokunbi   +7 more
doaj   +1 more source

Comparison of Hurst exponent estimation methods

open access: yesJournal of Economics and Administrative Sciences, 2021
Through recent years many researchers have developed methods to estimate the self-similarity and long memory parameter that is best known as the Hurst parameter. In this paper, we set a comparison between nine different methods. Most of them use the deviations slope to find an estimate for the Hurst parameter like Rescaled range (R/S), Aggregate ...
Amjad H. Hamza, Munaf Y. Hmood
openaire   +2 more sources

Typical Algorithms for Estimating Hurst Exponent of Time Sequence: A Data Analyst’s Perspective

open access: yesIEEE Access
The Hurst exponent is a significant metric for characterizing time sequences with long-term memory property and it arises in many fields such as physics, engineering, mathematics, statistics, economics, psychology, and so on.
Hong-Yan Zhang   +3 more
doaj   +1 more source

Analysis of Solar Neutrino Data from Super-Kamiokande I and II

open access: yesEntropy, 2014
We are going back to the roots of the original solar neutrino problem: the analysis of data from solar neutrino experiments. The application of standard deviation analysis (SDA) and diffusion entropy analysis (DEA) to the Super-Kamiokande I and II data ...
Hans J. Haubold   +2 more
doaj   +1 more source

On the utility of the hurst exponent in predicting future crises

open access: yesCorporate Ownership and Control, 2012
The aim of this article is to ascertain whether and to what extent the Hurst exponent can be used to forecast future crises. The first and second sections focus on the Hurst exponent, giving theoretical insights and a summary of its uses in finance. The analysis of a dataset of 35 indices and stocks representing various geographical areas and economic ...
Coen T., Piovani G, TORLUCCIO, GIUSEPPE
openaire   +2 more sources

Investigation of non linear dynamics of an excitable magnetron sputtering plasma

open access: yesResults in Physics, 2019
In this paper nonlinear dynamical behaviour of an excitable DC magnetron sputtering plasma has been investigated. Initially, plasma exhibited fixed point dynamics whereas with the increase in the discharge voltage, spikes were observed in the floating ...
Gopikishan Sabavath   +4 more
doaj   +1 more source

Anomaly Detection in Fractal Time Series with LSTM Autoencoders

open access: yesMathematics
This study explores the application of neural networks for anomaly detection in time series data exhibiting fractal properties, with a particular focus on changes in the Hurst exponent.
Lyudmyla Kirichenko   +3 more
doaj   +1 more source

Self-affine subglacial roughness: consequences for radar scattering and basal water discrimination in northern Greenland [PDF]

open access: yesThe Cryosphere, 2017
Subglacial roughness can be determined at a variety of length scales from radio-echo sounding (RES) data either via statistical analysis of topography or inferred from basal radar scattering.
T. M. Jordan   +6 more
doaj   +1 more source

A fast algorithm for robust estimates of the Hurst exponent when analyzing small samples of biometric and market data

open access: yesИзвестия высших учебных заведений. Поволжский регион:Технические науки
Background. Currently, the Hurst exponent is quite easily interpreted in relation to biometric, medical and economic data, but it is customary to evaluate it on large samples.
V.I. Volchikhin   +3 more
doaj   +1 more source

Deep Neural Network Model for Hurst Exponent: Learning from R/S Analysis

open access: yesMathematics
This paper proposes a deep neural network (DNN) model to estimate the Hurst exponent, a crucial parameter in modelling stock market price movements driven by fractional geometric Brownian motion.
Luca Di Persio, Tamirat Temesgen Dufera
doaj   +1 more source

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