| 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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| Files |
| Filename |
Size |
Approximate Download Time
(Hours:Minutes:Seconds)
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56K Modem |
ISDN (64 Kb) |
ISDN (128 Kb) |
Higher-speed Access |
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Thesis_Edwards.pdf |
3.25 Mb |
00:15:02 |
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