Multifractal wavelet model for positive processes

Matthew S. Crouse, Rudolf H. Riedi, Vinay J. Ribeiro, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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 positivity, which results in a swift O(N) algorithm to synthesize N-point data sets. We elucidate our model's ability to capture the covariance structure of real data, study its multifractal properties, and derive a scheme for matching it to real data observations. We demonstrate the model's utility by applying it to network traffic synthesis. The flexibility and accuracy of the model and fitting procedure result in a close match to the real data statistics (variance-time plots) and queuing behavior.

Original languageEnglish
Title of host publicationProceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis
Place of PublicationPiscataway, NJ, United States
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages341-344
Number of pages4
StatePublished - Jan 1 1998
EventProceedings of the 1998 IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis - Pittsburgh, PA, USA
Duration: Oct 6 1998Oct 9 1998

Other

OtherProceedings of the 1998 IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis
CityPittsburgh, PA, USA
Period10/6/9810/9/98

ASJC Scopus subject areas

  • Engineering(all)

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