Edwards, Samuel Zachary2014-03-142014-03-142006-05-15etd-05252006-085100http://hdl.handle.net/10919/33223The 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.In CopyrightLong-range DependenceSelf-SimilarityShort-range DependenceHurst parameterTime Series AnalysisFractalsWaveletsForecastThesishttp://scholar.lib.vt.edu/theses/available/etd-05252006-085100/