Forecasting Models for Steel Demand Uncertainty Using Bayesian Methods
A forecasting model for steel demand uncertainty in Thailand is proposed. It consists of trend, autocorrelation, and outliers in a hierarchical Bayesian frame work. The proposed model uses a cumulative Weibull distribution function, latent first-order autocorrelation, and binary selection, to account for trend, time-varying autocorrelation, and outliers, respectively. The Gibbs sampling Markov Chain Monte Carlo (MCMC) is used for parameter estimation. The proposed model is applied to steel demand index data in Thailand. The root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) criteria are used for model comparison. The study reveals that the proposed model is more appropriate than the exponential smoothing method.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1094018Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF
 ISIT. (2014, Jan 22). "Thailand economic and steel industry”. Available: http://www.oecd.org/sti/ind/50494696.pdf.
 O. I. E., (2013, Sep 29). "Manufacture of basic iron and steel”. Available: http://www.oie.go.th/en/academic/index.
 P. Congdon, Applied Bayesian Modelling, John Wiley & Sons: New York, 2003.
 W.W. Moe and P.S. Fader, "Using advance purchase orders to forecast new product sales,” Marketing Science, Vol. 21, No. 3, 2002, pp. 347-364.
 M. West and P.J. Harrison, Bayesian Forecasting and Dynamic Models, 2nd ed., New York: Springer-Verlag, 1997.
 P.M. Yelland, "Bayesian Forecasting of Part Demand,” International Journal of Forecasting, Vol. 26, 2010, pp. 374-396.
 P. Tongkhow and N. Kantanantha, "Bayesian Model for Time Series with Trend, Autoregression and Outliers,” Proceedings of the ICT&KE 2012, IEEE, Bangkok Thailand, 2012, pp. 90-94, 2012.
 A. Gelman, "Prior distributions for variance parameters in hierarchical models,” Bayesian Analysis, Vol. 1, No. 3, 2006, pp. 515–533.
 P. Tongkhow and N. Kantanantha, "Appropriate forecasting models for fluctuating vegetable prices in Thailand,” IEEE Proceedings, The CET 2011, Shanghai, 2011, pp.162-166.
 P. Congdon, Bayesian Statistical Modelling, 2nd ed., John Wiley & Sons: New York, 2006.
 C.P. Robert and G. Casella, Monte Carlo Statistical Methods, 2nd ed., Springer: New York, 2004.