Forecasting oil palm and crude palm oil data in Thailand using exponential time-series methods
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Abstract
In this paper, several exponential time-series methods are applied to forecast oil palm prices, crude palm oil prices, and crude palm oil production in Thailand from January to March 2018. The selected methods include Double Exponential Smoothing (DES), the Multiplicative Holt-Winters (MHW), the Additive Holt-Winters (AHW), the Improved Additive Holt-Winters (IAHW), and the Extended Additive Holt-Winters (EAHW) methods. Input data from January 2005 to December 2017 (thirteen years) were collected from the databases of the Office of Agricultural Economics and the Department of Internal Trade. Here, the novelty of our work is twofold. First, three closely related input data types are forecasted and analyzed simultaneously. Second, the well-known time-series forecasting methods (i.e. the DES, the MHW, and the AHW methods) and the efficient methods recently proposed in the literature (i.e. the IAHW and the EAHW methods) are implemented and tested. Therefore, the best forecast results determined by optimal methods are revealed. Our study demonstrates that the DES and the EAHW methods provide the smallest error (measured by Mean Absolute Percentage Error, MAPE) in forecasting oil palm and crude palm oil prices. For crude palm oil production, the IAHW and the EAHW yield better performance. The results also show the trends of the average monthly and yearly data and indicate that during January to March 2018, crude palm oil production in Thai markets should increase and prices will likely be stable. We believe that our research methodology and results can be useful for planning and setting strategy for Thai agriculturists and the government.
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References
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