A Comparison of Forecasting Methods for Sugarcane Prices

Abstract

This research aimed to compare the sugarcane prices forecast model by six statistical forecasting methods: Holt’s exponential smoothing method, Damped trend exponential smoothing method, simple seasonal exponential smoothing method, Winters’ additive exponential smoothing method, Winters’ multiplicative exponential smoothing method, and combined forecasting method in order to create the best forecasting model. Time series of monthly sugarcane prices were gathered from the website of Office of Agricultural Economics during January 2005 to August 2018 of 164 values. The criterion of the lowest mean absolute percentage error was used for comparing the accuracy of the forecasting model. Research findings indicated that for all forecasting methods that had been studied, the most appropriate method was simple seasonal exponential smoothing method and the forecasting model was  Y^_t = 794.134160 + ^_t where Y^_t  represents the forecast value at time t and S^_t represents the seasonal index at time t .