A comparative assessment of fuzzy regression models: the case of oil consumption estimation. (14th February 2011)

• Record Type: Journal Article
• Authors: Azadeh, Ali; Seraj, Oveis; Saberi, Morteza
• Is Part Of: International journal of industrial and systems engineering. Volume 7:Number 2(2011)
• Page Start: 195
• Page End: 223
• Publication Date: 2011-02-14
• Subjects: fuzzy regression -- fuzzy mathematical programming -- oil consumption estimation; Systems engineering -- Periodicals; Industrial engineering -- Periodicals; 620.001171
• Languages: English
• ISSNs: 1748-5037

Abstract

The objective of this study is to examine the most well-known FR approaches with respect to oil consumption estimation. Furthermore, there is no clear cut as to which approach is superior for oil consumption estimation. The economic indicators used in this paper are population, cost of crude oil, gross domestic production and annual oil production. The data for oil consumption in Canada, USA, Japan and Australia from 1990 to 2005 are considered. The input data are divided into train and test data. The FR models have been tuned for all their parameters according to the train data and the best coefficients are identified. Three popular defuzzification methods for defuzzifying outputs are applied. For determining the rate of error of FR models estimations, mean absolute percentage error is calculated. This study reveals that there is no best FR model unlike previous studies which claim to have developed the most efficient FR models.