Short-Term Compumetric Forecast of Crude Oil Prices

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "M. A. Kaboudan",
  title =        "Short-Term Compumetric Forecast of Crude Oil Prices",
  editor =       "R. Neck",
  booktitle =    "Modeling and Control of Economic Systems 2001 -- A
                 Proceedings volume from the 10th IFAC Symposium",
  publisher =    "Elsevier Science Ltd",
  year =         "2003",
  pages =        "365--370",
  address =      "Klagenfurt, Austria",
  publisher_address = "Oxford",
  month =        "6-8 " # sep,
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-08-043858-0",
  DOI =          "doi:10.1016/B978-008043858-0/50062-0",
  URL =          "",
  abstract =     "Forecasting oil prices remains an important empirical
                 issue. This paper compares three forecasts of
                 short-term oil prices using two compumetric methods and
                 naive random walk. Compumetric methods use model
                 specifications generated by computers with limited
                 human intervention. Users are responsible only for
                 selecting the appropriate set of explanatory variables.
                 The compumetric methods employed here are genetic
                 programming and artificial neural networks. The
                 variable to forecast is monthly US imports FOB oil
                 prices. Each method is used to forecast one and three
                 months ahead. The results suggest that neural networks
                 deliver better predictions.",

Genetic Programming entries for Mahmoud A Kaboudan