Now showing items 1-4 of 4
Fast, Exact Synthesis of Gaussian and nonGaussian Long-Range-Dependent Processes
1/<i>f</i> noise and statistically self-similar processes such as fractional Brownian motion (fBm) are vital for modeling numerous real-world phenomena, from network traffic to DNA to the stock market. Although several ...
A Multifractal Wavelet Model with Application to Network Traffic
In this paper, we develop a new multiscale modeling framework for characterizing positive-valued data with long-range-dependent correlations (1/f noise). Using the Haar wavelet transform and a special multiplicative structure ...
Simulation of Non-Gaussian Long-Range-Dependent Traffic using Wavelets
In this paper, we develop a simple and powerful multiscale model for the synthesis of non-Gaussian, long-range dependent (LRD) network traffic. Although wavelets effectively decorrelate LRD data, wavelet-based models have ...
Network Traffic Modeling using a Multifractal Wavelet Model
In this paper, we describe a new multiscale model for characterizing positive-valued and long-range dependent data. The model uses the Haar wavelet transform and puts a constraint on the wavelet coefficients to guarantee ...