Modelling and Forecasting Daily Rubber Smoked Sheet Prices
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Abstract
This paper presents the comparison between transfer function method and neutral networks for predicting rubber smoked sheet prices. The two procedures are based on time series data extracted from 2001 to 2003. The accuracy of two forecasting models is evaluated using (1) root mean squared error: RMSE and (2) mean absolute percentage error: MAPE. The results indicate that the traditional statistical approach has a potential to forecast rubber smoked sheet prices compared to neural networks model. In addition, the future directions are also discussed in the paper.
Keywords: Time series, Transfer function method, Neutral networks, Box and Jenkins method, Rubber smoked sheet (RSS3) prices, Back propagation
Corresponding author: E-mail: walailak_ut@yahoo.com , me_porntip@yahoo.com
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References
[2] Delurgio, S.A. 1998. Forecasting Principles and Applications. Singapore, McGraw-Hill.
[3] Montgomery, D.C. and Johnson, L.A. 1977, Forecasting and Time Series Analysis. New York, John Wiley and Sons.
[4] Makridakis, S., Wheelwright, S.C. and Hyndman, R.J. 1998. Forecasting Method and Applications. 3rd Edition, New York, John Wiley and Sons.
[5] Supasiripinyo, S. 2000. An Analysis of Price Movement of Rubber Smoke Sheet Grade 3 of Thailand Master thesis, Ramkhamhaeng University.
[6] Chatchaipun, P. and Atthirawong, W. 2003. Time Series Forecasting of Rubber Smoked Sheet Prices: A Comparative Study of Box and Jenkins and Transfer Function Methods. Proceedings of 1st Operations Research Conference, Kasetsart University, Bangkok, pp. 7-17.
[7] Hill, T. et al. 1996. Neural Network Models for Time Series Forecasts. Management Science. 42(7), pp. 1082-1092.
[8] Fausett, L. 1994. Fundamental of Neural Networks: Architectures Algorithms and Applications. London, Prentice-Hall International.
[9] Haykin, S. 1994. Neural Networks: A Comprehensive Foundation. New York, Macmillan College.
[10] www.http://www.rubberthai.co.th/rubberprice.2001.html.
[11] www.http://www.rubberthai.co.th/rubberprice.2002.html.
[12] www.http://www.rubberthai.co.th/rubberprice.2003.html.
[13] Phetgran, S. 1989. The Relationship of Natural Rubber Prices in Various Market Levels in Thailand and Effecting Factors. Master thesis, Kasetsart University.
