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Erschienen in:
Cluster Validation Analysis on Attribute Relative of Soft-Set Theory Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Forecasting of Malaysian Oil Production and Oil Consumption Using Fuzzy Time Series

verfasst von : Riswan Efendi, Mustafa Mat Deris

Erschienen in: Recent Advances on Soft Computing and Data Mining

Verlag: Springer International Publishing

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Abstract

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.

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Metadaten
Titel
Forecasting of Malaysian Oil Production and Oil Consumption Using Fuzzy Time Series
verfasst von
Riswan Efendi
Mustafa Mat Deris
Copyright-Jahr
2017
Buch
Recent Advances on Soft Computing and Data Mining

Print ISBN: 978-3-319-51279-2

Electronic ISBN: 978-3-319-51281-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-51281-5_4