Forecasting quarterly US demand for natural gas

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Mahmoud A. Kaboudan and Qingfeng ``Wilson'' Liu",
  title =        "Forecasting quarterly US demand for natural gas",
  journal =      "Information Technology for Economics and Management",
  year =         "2004",
  volume =       "2",
  number =       "1",
  email =        "",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1643-8949",
  URL =          "",
  URL =          "",
  size =         "14 pages",
  abstract =     "forecasting demand for natural gas in the short run.
                 The method used combines genetic programming with a
                 two-stage least squares (2SLS) regression system of
                 equations. In the system developed, each of US
                 consuming sectors is represented by a regression model.
                 These models quantify each sector's demand elasticity
                 and produce a four-year-ahead forecast of quarterly
                 consumption of gas. Genetic programming (GP) is used
                 here to obtain accurate predictions of exogenous
                 variables to use as inputs into the 2SLS system of
                 equations. GP is a computerised search algorithm that
                 identifies equations that can forecast well. The
                 proposed method delivered interesting nonlinear
                 equations that seem to produce a reasonable forecast.",
  notes =        "",

Genetic Programming entries for Mahmoud A Kaboudan Qingfeng "Wilson" Liu