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Petroleum demand forecasting for Taiwan using modified fuzzy‐grey algorithms
Author(s) -
Lu ShinLi,
Tsai ChenFang
Publication year - 2016
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12129
Subject(s) - ewma chart , residual , computer science , mean absolute error , fuzzy logic , mean absolute percentage error , algorithm , absolute deviation , statistics , mean squared error , artificial intelligence , mathematics , process (computing) , artificial neural network , control chart , operating system
In this paper, we adopt the exponentially weighted moving average (EWMA) method to develop the residual modification EWMA grey forecasting model REGM(1,1) and combines it with fuzzy theory to derive the fuzzy REGM or the FREGM(1,1) model. The proposed model is used to forecast annual petroleum demand in Taiwan. The experimental results show that the mean absolute percentage errors, median absolute percentage error, and symmetric mean absolute percentage error of FREGM(1,1) model are higher by 23.71, 12.26, and 23.06% respectively, compared with those obtained using the traditional GM(1,1) model.