Many statistical models have been implemented in the energy sectors, especially in the oil production and oil consumption. However, these models required some assumptions regarding the data size and the normality of data set. These assumptions give impact to the forecasting accuracy. In this paper, the fuzzy time series (FTS) model is suggested to solve both problems, with no assumption be considered. The forecasting accuracy is improved through modification of the interval numbers of data set. The yearly oil production and oil consumption of Malaysia from 1965 to 2012 are examined in evaluating the performance of FTS and regression time series (RTS) models, respectively. The result indicates that FTS model is better than RTS model in terms of the forecasting accuracy.
CITATION STYLE
Efendi, R., & Deris, M. M. (2017). Forecasting of malaysian oil production and oil consumption using fuzzy time series. In Advances in Intelligent Systems and Computing (Vol. 549 AISC, pp. 31–40). Springer Verlag. https://doi.org/10.1007/978-3-319-51281-5_4
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