- author = "M. A. Kaboudan",
- title = "Compumetric Forecasting of Crude Oil Prices",
- booktitle = "Proceedings of the 2001 Congress on Evolutionary Computation CEC2001",
- year = "2001",
- pages = "283--287",
- address = "COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, Korea",
- publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA",
- month = "27-30 " # may,
- organisation = "IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE)",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, ANN, computer forecasting, crude oil prices, forecasting models, monthly forecasts, random walk type, commodity trading, forecasting theory, neural nets",
- ISBN = "0-7803-6658-1",
-
DOI = "
doi:10.1109/CEC.2001.934402",
- abstract = "This paper contains short term monthly forecasts of crude oil prices using computerised methods. Compumetric forecasting methods are ones that use computers to identify the underlying model that produces the forecast. Typically, forecasting models are designed or specified by humans rather than machines. Compumetric methods are applied to determine whether models they provide produce reliable forecasts. Forecasts produced by two compumetric methods-genetic programming and artificial neural networks-are compared and evaluated relative to a random walk type of prediction. The results suggest that genetic programming has advantage over random walk predictions while the neural network forecast proved inferior",
-
notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
IEEE Catalog Number = 01TH8546C,
Library of Congress Number =",
