AN EVALUATION OF HOLT-WINTERS METHODS WITH DIFFERENT INITIAL TREND VALUES FOR FORECASTING CRUDE PALM OIL PRODUCTION AND PRICES IN THAILAND
Keywords:
Holt-Winters methods, initial trend values, forecasting, crude palm oil productions, prices, ThailandAbstract
An evaluation of the Holt-Winters methods with different initial trend values for forecasting crude palm oil production and prices in Thailand is presented in this paper. The Holt-Winters methods including well-known methods (i.e. the multiplicative Holt-Winters (MHW) and the additive Holt-Winters (AHW) methods) and the extended version of the AHW method proposed in the literature (i.e. the extended additive Holt-Winters (EAHW) method) are selected and tested, while 4 different initial trend values are set and applied for such methods. By this procedure, the major contributions of our paper are how: a) the different initial trend values that affect forecasting accuracy of the selected methods can be investigated, and b) the best forecast results determined by the optimal solution are obtained. The input data from January 2005 to February 2018 (158 months) are collected from the database of the Department of Internal Trade, Ministry of Commerce, Thailand. The results show that the EAHW method provides good accuracy as indicated by the mean absolute percentage error in forecasting crude palm oil production and prices. Additionally, the results demonstrate that, by applying the different initial trend values to the forecasting methods, the level of forecasting accuracy is significantly different. Finally, we also provide the forecast results of March 2018 to May 2018 in this paper.
References
Alon, I., Qi, M., and Sadowski, R.J. (2001). Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods. J. Retail. Cons. Serv., 8(3):147-156.
Booranawong, T. and Booranawong, A. (2017a). An exponentially weighted moving average method with designed input data assignments for forecasting lime prices in Thailand. J. Teknologi., 79(6):53-60.
Booranawong, T. and Booranawong, A. (2017b). Simple and double exponential smoothing methods with designed input data for forecasting a seasonal time series: in an application for lime prices in Thailand. Suranaree J. Sci. Technol., 24(3):301-310.
Chatfield, C. (2001). Time-series Forecasting. Chapman & Hall, NY, USA, 400p.
Dhakre, D.S., Sarkar, K.A., and Manna, S. (2016). Forecasting price of Brinjal by Holt Winters method in west Bengal using MS Excel. International J. Bio-res., Environ. Agri. Sci., 2(1):232-235.
Gelper, S., Fried, R., and Croux, C. (2010). Robust forecasting with exponential and Holt-Winters smoothing. J. Forecasting, 29:285-300.
Gentry, T.W., Wiliamowski, B.M., and Weatherford, L.R. (1995). A comparison of traditional forecasting techniques and neural networks. Intelligent Engineering Systems through Artificial Neural Networks. 5:765-770.
Holt, C.C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. Int. J. Forecasting., 20(1):5-10.
Kalekar, P.S. (2004). Time series forecasting using Holt-Winters exponential smoothing. Kanwal Rekhi School of Information Technology, 4329008,1-13.
Kanchymalay, K., Salim, N., Sukprasert, A., Krishnan, R., and Hashim, U.R. (2017). Multivariate time series forecasting of crude palm oil price using machine learning techniques. IOP Conf. Ser.-Mat. Sci., 226:1-9.
Montgomery, D.C., Jennings, C.L. and Kulahci, M. (2008). Introduction to Time Series Analysis and Forecasting. Wiley-Interscience, Hoboken, NJ, USA, 445p.
Petchseechoung, W. (2017). Thailand Industry Outlook 2017-19: Oil Palm Industry. Krungsri Research, Bank of Ayudhya Public Company, Ltd., Bangkok, Thailand, 8p.
Ramos, P., Santos, N., and Rebelo, R. (2015). Performance of state space and ARIMA models for consumer retail sales forecasting. Robot. Cim-Int. Manuf., 34:151-163.
Ravindran, A. and Warsing, D.P. (2013). Supply Chain Engineering: Models and Applications. CRC Press, Boca Raton, FL, USA, 546p.
Siregar, B., Butar-Butar, A.I., Rahmat, R.F., Andayani, U., and Fahmi, F. (2017). Comparison of exponential smoothing methods in forecasting palm oil real production. J. Phys. Conf. Ser., 801:1-9.
The Department of Internal Trade. (2018). Ministry of Commerce, Bangkok, Thailand. Available from http://www.dit.go.th/. Accessed date: March 15, 2018.
Tratar, L.F., Mojskerc, B., and Toman, A. (2016). Demand forecasting with four-parameter exponential smoothing. Int. J. Prod. Econ., 181:162-173.
Tratar, L.F. and Srmcnik, E. (2016). The comparison of Holt-Winters method and regression methods: a case study. Energy, 109:266-276.
Wan Ahmad, W.K.A. and Ahmad, S. (2013). Arima model and exponential smoothing method: a comparison. AIP Conf. Proc., 1522:1,312-1,321.
