Title page for ETD etd-05252006-085100


Type of Document Master's Thesis
Author Edwards, Samuel Zachary
Author's Email Address pegandsam@mac.com
URN etd-05252006-085100
Title Forecasting Highly-Aggregate Internet Time Series Using Wavelet Techniques
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Mili, Lamine M. Committee Chair
Bell, Amy E. Committee Member
DaSilva, Luiz A. Committee Member
Keywords
  • Long-range Dependence
  • Self-Similarity
  • Short-range Dependence
  • Hurst parameter
  • Time Series Analysis
  • Fractals
  • Wavelets
  • Forecast
Date of Defense 2006-05-15
Availability unrestricted
Abstract
The U.S. Coast Guard maintains a network structure to connect its nation-wide assets. This paper analyzes and models four highly aggregate traces of the traffic to/from the Coast Guard Data Network ship-shore nodes, so that the models may be used to predict future system demand. These internet traces (polled at 5’40” intervals) are shown to adhere to a Gaussian distribution upon detrending, which imposes limits to the exponential distribution of higher time-resolution traces. Wavelet estimation of the Hurst-parameter is shown to outperform estimation by another common method (Sample-Variances). The First Differences method of detrending proved problematic to this analysis and is shown to decorrelate AR(1) processes where 0.65< phi1 <1.35 and correlate AR(1) processes with phi1 <-0.25. The Hannan-Rissanen method for estimating (phi,theta) is employed to analyze this series and a one-step ahead forecast is generated.

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