Hybrid Model for Forecasting Monthly Price of Maize in Thailand
Abstract
This research aimed to create and select a forecast model suitable for the time series data on the monthly price of maize in Thailand. The data were obtained from the website of the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives from January 1999 to November 2021, a total of 275 values. The data were divided into two sets. The first set consisted of 252 values of the training data from January 1999 to December 2019. This data set was used for creating the forecast model using the SARIMA model and the Hybrid SARIMAGASVR model. The second set consisted of 23 values of the test data from January 2020 to November 2021. It was used for comparing the accuracy of the forecast model using the Mean Absolute Percentage Error
(MAPE). The results showed that the Hybrid SARIMAGASVR model, which was a hybrid of the SARIMA model and the Support Vector Regression model (SVR), was more accurate than the SARIMA model, with a MAPE value of 0.02765259. Therefore, it can be concluded that the hybrid model proposed in this research is suitable for forecasting the time series data on the monthly price of maize in Thailand.
Keywords : price of maize ; hybrid model ; SARIMA model ; Support Vector Regression model
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