MODELING NETWORK TRAFFIC USING CAUCHY CORRELATION MODEL WITH LONG-RANGE DEPENDENCE
From MaRDI portal
Publication:5706546
DOI10.1142/S0217984905008864zbMath1076.90010WikidataQ59204149 ScholiaQ59204149MaRDI QIDQ5706546
No author found.
Publication date: 21 November 2005
Published in: Modern Physics Letters B (Search for Journal in Brave)
time seriesfractal dimensionlong-range dependenceHurst parameternetwork trafficCauchy correlation model
Related Items (max. 100)
A class of negatively fractal dimensional Gaussian random functions ⋮ Analytic and asymptotic properties of multivariate generalized Linnik's probability densities ⋮ Fractal time series -- A tutorial review
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Functional analysis and linear control theory
- Modeling autocorrelation functions of self-similar teletraffic in communication networks based on optimal approximation in Hilbert space
- A correlation-based computational model for synthesizing long-range dependent data.
- Wavelet analysis and synthesis of fractional Brownian motion
- Fractal Analysis of Surface Roughness by Using Spatial Data
- A power-law model and other models for long-range dependence
- Stochastic Models That Separate Fractal Dimension and the Hurst Effect
- Self-similar processes in communications networks
- Spectral representation of fractional Brownian motion in n dimensions and its properties
- Fractal estimation from noisy data via discrete fractional Gaussian noise (DFGN) and the Haar basis
- The Impact of Autocorrelation on Queuing Systems
- A fast estimation algorithm on the Hurst parameter of discrete-time fractional Brownian motion
- Fractional Brownian Motions, Fractional Noises and Applications
- Gaussian sample functions and the Hausdorff dimension of level crossings
- Inhomogeneous scaling behaviors in Malaysian foreign currency exchange rates
This page was built for publication: MODELING NETWORK TRAFFIC USING CAUCHY CORRELATION MODEL WITH LONG-RANGE DEPENDENCE
