An integrated GA-time series algorithm for forecasting oil production estimation: USA, Russia, India, and Brazil

A. Azadeh, M. Aramoon and M. Saberi

International Journal of Industrial and Systems Engineering, 2009, vol. 4, issue 4, 368-387

Abstract: This study presents an integrated algorithm for forecasting oil production based on a Genetic Algorithm (GA) with variable parameters using stochastic procedures, time series and Analysis of Variance (ANOVA). The significance of the proposed algorithm is two fold. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted GA model based on Minimum Absolute Percentage Error (MAPE) or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future oil production forecasting because of its dynamic structure, whereas previous studies assume that GA always provides the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the data for oil production in USA, Russia, India and Brazil from 2001 to 2006 are used and applied to the proposed algorithm.

Keywords: integrated genetic algorithms; GAs; time series; oil production; ANOVA; analysis of variance; Duncan's multiple range test; MAPE; minimum absolute percentage error; forecasting; USA; Russia; India; Brazil. (search for similar items in EconPapers)
Date: 2009
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