The objective of this study was to forecast the wollongong prices via the use of statistical methods. The monthly average data, which were gathered from the website of the Office of Agricultural Economics during January 2005 to May 2020 of 185 months were divided into 2 datasets. The first dataset, which consisted of 180 months from January 2005 to December 2019 was used for constructing the forecasting models via the use of 10 statistical methods, namely, Box-Jenkins method, Holt's exponential smoothing method, Brown's exponential smoothing method, damped trend exponential smoothing method, simple seasonal exponential smoothing method, Winters' additive exponential smoothing method, Winters' multiplicative exponential smoothing method, additive decomposition method, multiplicative decomposition method, and combined forecasting method. The second dataset, which consisted of 5 months from January to May 2020 was used for comparing the accuracy of the forecasting model via the lowest root mean square error. The results indicated that the most accurate method was the Winters' additive exponential smoothing method.
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