Ginno Millan's picture
Real name: 
First Name: 
Ginno
Last Name: 
Millan
Affiliation: 
Universidad de Santiago de Chile
Job Title: 
Researcher
Expertise: 
Big Data, Complex Systems, Data Science, Deep Learning, Machine Learning, Telematics.
Short Bio: 
He is an Electronic Engineer (2001) and a Master in Engineering Sciences (2009), from the Pontificia Universidad Católica de Valparaíso, Chile, and a Doctor of Engineering Sciences (2013) from the University of Santiago de Chile.

Datasets & Competitions

A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long-range dependence (LRD) is presented by means of the formulation of a model that considers the use of piecewise affine onedimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.

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This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent.

Categories:
352 Views