Skip to main content

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

  • Conference paper
  • First Online:
Recent Advances on Soft Computing and Data Mining (SCDM 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 549))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

References

  1. Economic Transformation Programme Chap. 1. http://www.etp.pemandu.gov.my

  2. Gabralla, L.A., Abraham, A.: Computational modeling of crude oil price forecasting: a review of two decades of research. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 5, 729–740 (2013)

    Google Scholar 

  3. Kimura, S.: The 2nd ASEAN Energy Outlook. The Energy Data and Modeling Centre, Japan (2009)

    Google Scholar 

  4. Washington State Department of Transportation – Economic Analysis: Statewide Fuel Consumption Forecast Models (2010)

    Google Scholar 

  5. US Energy Information Administration: Short-Term Energy Outlook. Independent Statistics & Analysis (2015). Czajkowski, K., Fitzgerald, S., Foster, I., Kesselman, C.: Grid information services for distributed resource sharing. In: 10th IEEE International Symposium on High Performance Distributed Computing, pp. 181–184. IEEE Press, New York (2001)

    Google Scholar 

  6. Liangyong, F., Junchen, L., Xiongqi, P., Xu, T., Lin, Z.: Peak oil models forecast China’s oil supply, demand. Oil Gas J. 43–47 (2008)

    Google Scholar 

  7. Chiroma, H., et al.: An intelligent modeling of oil consumption. In: El-Alfy, El-Sayed M., Thampi, S.M., Takagi, H., Piramuthu, S., Hanne, T. (eds.). AISC, vol. 320, pp. 557–568. Springer, Cham (2015). doi:10.1007/978-3-319-11218-3_50

    Chapter  Google Scholar 

  8. Song, Q., Chissom, B.S.: Fuzzy time series and its models. Fuzzy Sets Syst. 54, 269–277 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  9. Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series – Part 1. Fuzzy Sets Syst. 54, 1–9 (1993)

    Article  Google Scholar 

  10. Chen, S.M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst. 81, 311–319 (1996)

    Article  Google Scholar 

  11. Singh, S.R.: A robust method for forecasting based on fuzzy time series. Int. J. Comput. Math. 188, 472–484 (2007)

    MathSciNet  MATH  Google Scholar 

  12. Kuo, I.: An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Syst. Appl. 36, 6108–6117 (2009)

    Article  Google Scholar 

  13. Ismail, Z., Efendi, R.: Enrollment forecasting based on modified weight fuzzy time series. J. Artif. Intell. 4, 110–118 (2011)

    Article  Google Scholar 

  14. Ismal, Z., Efendi, R., Deris, M.M.: Inter-quartile range approach to length – interval adjustment of enrollment data. Int. J. Comput. Intell. Appl. 3, 10 p. (2013)

    Google Scholar 

  15. Yu, H.K.: Weighted fuzzy time series models for TAIEX forecasting. Phys. A 349, 609–624 (2005)

    Article  Google Scholar 

  16. Yu, H.K., Huarng, K.H.: A bivariate fuzzy time series model to forecast the TAIEX. Expert Syst. Appl. 34, 2945–2952 (2008)

    Article  Google Scholar 

  17. Lee, H.L., Liu, A., Chen, W.S.: Pattern discovery of fuzzy time series for financial prediction. IEEE Trans. Syst. Man Cybern. Part B 18, 613–625 (2006)

    Google Scholar 

  18. Efendi, R., Ismail, Z., Deris, M.M.: Improved weight fuzzy time series used in the exchange rates forecasting US Dollar to Ringgit Malaysia. Int. J. Comput. Intell. Appl. 12, 19 p. (2013)

    Google Scholar 

  19. Bolturuk, E., Oztayzi, B, Sari, I.U.: Electricity consumption forecasting using fuzzy time series. In: IEEE 13th International Symposium on Computer Intelligence and Informatics, 20–22 November 2012, Istanbul, Turkey, pp. 245–249

    Google Scholar 

  20. Alpaslan, F., Cagcag, O.: Seasonal fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering. J. Soc. Econ. Stat. 2, 1–13 (2012)

    Google Scholar 

  21. Azadeh, A., Saberi, M., Gitiforouz, A.: An integrated simulation-based fuzzy regression-time series algorithm for electricity consumption estimation with non-stationary data. J. Chin. Inst. Eng. 34, 1047–1066 (2012)

    Article  Google Scholar 

  22. Efendi, R., Ismail, Z., Deris, M.M.: New linguistic out-sample approach of fuzzy time series for daily forecasting of Malaysian electricity load demand. Appl. Soft Comput. 28, 422–430 (2015)

    Article  Google Scholar 

  23. Wooldridge, J.M.: Introductory Econometrics A Modern Approach, 3rd edn. Thomson South Western, Mason (2006)

    Google Scholar 

Download references

Acknowledgment

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Riswan Efendi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-51279-2

  • Online ISBN: 978-3-319-51281-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics