A correlation-based computational model for synthesizing long-range dependent data.
type: Item
instance of: scholarly article
title: A correlation-based computational model for synthesizing long-range dependent data.
zbMATH Open document ID: 1051.93095
author: Ming Li
author: Chihung Chi
published in: Journal of the Franklin Institute
publication date: 2004-03-14T00:00:00Z
review text: A second-order stationary random function \(x(t)\) is called a stationary process with long-range dependent (LRD) data if \[ r_x(t)= E[x(t) x(t+\tau)]\sim c\tau^{2H- 2}\,(\tau\to \infty),\;H\in (0.5, 1), \] where \(c> 0\); the parameter \(H\) is called the Hurst parameter. The authors present a computation model to generate LRD data according to a given correlation structure by filtering white noise.
Mathematics Subject Classification ID: 93E03
Mathematics Subject Classification ID: 60G15
Mathematics Subject Classification ID: 60G10
zbMATH DE Number: 2056355
zbMATH Keywords: Long-range dependent data generation
zbMATH Keywords: Brownian motion
zbMATH Keywords: Fractional Gaussian noise
zbMATH Keywords: Filters
zbMATH Keywords: Fourier analysis
zbMATH Keywords: Teletraffic
zbMATH Keywords: Stationary process
zbMATH Keywords: Hurst parameter
reviewed by: Grigori N. Milstein
describes a project that uses: longmemo
MaRDI profile type: Publication
full work available at URL: j.jfranklin.2003.09.002
OpenAlex ID: W2011161232
Wikidata QID: Q59204160
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DOI: 10.1016/J.JFRANKLIN.2003.09.002
description: scientific article; zbMATH DE number 2056355