Forecasting Model for Para Rubber’s Export Sales

Main Article Content

Tatiporn Pattranurakyothin
Kanchana Kumnungkit*

Abstract

In this paper, monthly export values of para rubber are investigated using the Box-Jenkins method. To find the optimal predicting model, 12-year data, from January 2000 to December 2011, are used to analyze. Finally,the suitable mathematical model is seasonal ARIMA that use the analysis of time-series from the lowest level of the Mean Absolute Percent Error (MAPE). The best model is seasonal ARIMA (1,1,1) 1,1,012 .


Keywords: Mathematical model, Box-Jenkins Method, Exporting of Para rubber


E-mail: kkkancha@kmitl.ac.th

Article Details

Section
Original Research Articles

References

[1] Brockwell, P.J. and Davis, R.A., 1996. Introduction to Time Serie and Forecasting, Springer, New York.
[2] Chatfield, C., 2004. The Analysis of Time Series: An Introduction, 6th Ed, Chapman & Hall, New York.
[3] Tsekouras, G.J., Dialynas, E.N., Hatziargyriou, N.D. and Kavatza, S., 2007. A non-linear multivariable regression model for midterm energy of power systems. Electric Power Systems Res., 77: 1560-1568.
[4] Ghiassi, M., Zimbra, D.K. and Saidane, H., 2006. Medium term system load forecasting with a dynamic artificial neural network model. Electric Power System Res., 76: 302-316.
[5] Mohamed, N., Ahmad, M. H., Suhartono and Ismail, Z., 2011. Improving Short Term Load Forecasting Using Double Seasonal Arima Model., Indonesia. World Applied Sciences Journal 15(2): 223-231, 2011.
[6] Zhang, G. P. and Qi, M., 2 0 0 5 . Network Forecating for Seasonal and Trend Time Series, European Journal of Operation Research 160, 501-514.
[7] Mohamed, N., Ahmad, M. H., Ismail, Z. and Arshad, K. A., 2008. A multilayer feedforward neural network model and Box-Jenkins model for seasonal load forecasting. Ultra Scientist of Physical Scientist, International Journal of Physical Sciences. 20(3): 767-772.
[8] Makridakis, S., Wheelwright, S. C. and Hyndman, R. J., 1998. Forecasting: Methods and Application. John Willey and Sons, New York.
[9] Wei, W.W.S., 2006. Time Series Analysis, Univariate and Multivariate Methods, 2nd Ed. NewYork: Pearson, Addison Wesley.
[10] Box, G. E. P., Jenkins, G. M. and Reinsel, G. C., 2008. Time Series Analysis: Forecasting and Control, 4th Ed. New Jersey: John Wiley & Sons.
[11] Hobbs, B.F., Helman, U., Jipraikusarn, S., konda, S. and Maratukulan, D., 1998. Artificial neural networks for short-term energy forecasting : Accuracy and economic value. Neurocomputing, 23 :71-84.
[12] ดัช นีสิน ค้าอ อ ก จำ แ น ก ต าม กิจ ก ร ร ม ก าร ผ ลิต ( ล้าน บ าท ) . 2 0 1 0 . Bank of Thailand. At: http://www.bot.or.th/Thai/Statistics/EconomicAndFinancial/EconomicIndices/Pages/Index.aspx