Forecasting quarterly US demand for natural gas

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@Article{Kaboudan:2004:ITEM,

• 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 = "Mak_kaboudan@Redlands.edu",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1643-8949",
• URL = "http://www.item.woiz.polsl.pl/issue2.1/journal2.1.htm",
• URL = "http://www.item.woiz.polsl.pl/issue2.1/pdf/forecastingquarterlyusdemandfornaturalgas.pdf",
• 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 = "http://www.item.woiz.polsl.pl/",

}

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

Citations