USING STATISTICAL FORECASTING TECHNIQUES TO PREDICT THE SUGARCANE YIELD IN GUANGXI
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Abstract
This study aims to: 1) investigate and analyze the application of six methods, namely Trend Analysis, Decomposition, Moving Average, Simple Exponential Smoothing (Simple Exp Smoothing), Double Exponential Smoothing (Double Exp Smoothing), and Holt-Winters Method, in predicting the sugar cane yield in Guangxi, and compare the applicability and accuracy of these six forecasting methods; 2) this research will not only provide crucial reference for future sugar cane yield predictions in Guangxi but also offer scientific evidence and strategic planning ideas for decision-makers in Guangxi's sugar industry; 3) through theoretical and empirical research, fill the research gap where no researchers have yet applied these methods to predict sugar cane yield in Guangxi. The research results indicate that the Holt-Winters Method exhibits the highest forecasting accuracy, with a Mean Absolute Percentage Error (MAPE) of 17 and a Mean Absolute Deviation (MAD) of 189, significantly lower than those of the other five methods. Therefore, it is highly suitable for predicting sugar cane yield in the Guangxi Zhuang Autonomous Region.
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