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

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Recent Advances on Soft Computing and Data Mining (SCDM 2016)

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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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Acknowledgment

The authors are grateful to Research and Innovation Fund, UTHM for their financial support.

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Authors and Affiliations

  1. Faculty of Computer Science, Universiti Tun Hussein Onn Malaysia, 86400, Batu Pahat, Johor, Malaysia

    Riswan Efendi & Mustafa Mat Deris

  2. Mathematics Department, Faculty of Science and Technology, State Islamic University of Sultan Syarif Kasim Riau, Panam, 28294, Pekanbaru, Indonesia

    Riswan Efendi

Authors
  1. Riswan Efendi
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  2. Mustafa Mat Deris
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Correspondence to Riswan Efendi .

Editor information

Editors and Affiliations

  1. Department of Information System, University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Rozaida Ghazali

  3. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Nazri Mohd Nawi

  4. Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia

    Mustafa Mat Deris

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Efendi, R., Deris, M.M. (2017). Forecasting of Malaysian Oil Production and Oil Consumption Using Fuzzy Time Series. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_4

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

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