Forecasting Highly-Aggregate Internet Time Series Using Wavelet Techniques

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 5â 40â 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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2006

Author dc:creator

• Edwards, Samuel Zachary

Chair dc:contributor.committeechair

• Mili, Lamine M.

Committee members dc:contributor.committeemember

• DaSilva, Luiz A.
• Bell, Amy E.

• Long-range Dependence
• Self-Similarity
• Short-range Dependence
• Hurst parameter
• Time Series Analysis
• Fractals
• Wavelets
• Forecast

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

etd-05252006-085100

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/33223