Accession Number : ADA294227
Title : Forecasting Jet Fuel Prices Using Artificial Neural Networks.
Descriptive Note : Master's thesis,
Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA
Personal Author(s) : Kasprzak, Mary A.
PDF Url : ADA294227
Report Date : MAR 1995
Pagination or Media Count : 63
Abstract : Artificial neural networks provide a new approach to commodity forecasting that does not require algorithm or rule development. Neural networks have been deemed successful in applications involving optimization, classification, identification, pattern recognition and time series forecasting. With the advent of user friendly, commercially available software packages that work in a spreadsheet environment, such as Neural Works Predict by NeuralWare, more people can take advantage of the power of artificial neural networks. This thesis provides an introduction to neural networks, and reviews two recent studies of forecasting commodities prices. This study also develops a neural network model using Neural Works Predict that forecasts jet fuel prices for the Defense Fuel Supply Center (DFSC). In addition, the results developed are compared to the output of an econometric regression model, specifically, the Department of Energy's Short-Term Integrated Forecasting System (STWS) model. The Predict artificial neural network model produced more accurate results and reduced the contribution of outliers more effectively than the STIFS model, thus producing a more robust model.
Descriptors : *NEURAL NETS, *FORECASTING, *COSTS, *JET ENGINE FUELS, *ECONOMIC MODELS, COMPUTER PROGRAMS, MATHEMATICAL MODELS, ALGORITHMS, INTEGRATED SYSTEMS, OPTIMIZATION, TIME SERIES ANALYSIS, ACCURACY, THESES, REGRESSION ANALYSIS, SHORT RANGE(TIME), PATTERN RECOGNITION, ECONOMETRICS, USER FRIENDLY, COMMODITIES.
Subject Categories : Economics and Cost Analysis
Jet and Gas Turbine Engines
Distribution Statement : APPROVED FOR PUBLIC RELEASE