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 modelReferences
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Cheng, Y., Zhu, Q., Peng, Y., Huang, X.-F., & He, L.-Y. (2021). Multiple strategies for a novel hybrid forecasting algorithm of ozone based on data-driven models. Journal of Cleaner Production, 326, 1-15.
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Biology, Control, and Artificial Intelligence; University of Michigan Press: Ann Arbor, MI, USA.
Huang, X., Wang, J., & Huang, B. (2021). Two novel hybrid linear and nonlinear models for wind speed forecasting. Energy Conversion and Management, 238, 1-19.
Kao, Y.-S., Nawata, K., & Huang, C.-Y. (2020). Predicting Primary Energy Consumption Using Hybrid ARIMA
and GA-SVR Based on EEMD Decomposition. Mathematics, 8(10), 1 - 19.
Li, R., Hu, Y., Heng, J., & Chen, X. (2021). A novel multiscale forecasting model for crude oil price time series. Technological Forecasting and Social Change, 173, 1-15.
Liu, M.-D., Ding, L., & Bai, Y.-L. (2021). Application of hybrid model based on empirical mode decomposition, novel recurrent neural networks and the ARIMA to wind speed prediction. Energy Conversion and Management, 233, 1 - 19.
Liu, Z., Jiang, P., Zhang, L., & Niu, X. (2020). A combined forecasting model for time series: Application to short-term wind speed forecasting. Applied Energy, 259, 1 - 25.
National Statistical Office Thailand. (2021). Population and Housing Census. Retrieved December
10, 2021, from http://www.nso.go.th/sites/2014en/censussurvey.
Riansut, W., & Thongrit, K. (2017). Forecasting the Price of Field Corn in Thailand. RUMTP Research Journal,
11(1), 1-14. (in Thai)
Singchai, P., & Keeratiwintakorn, P. (2014). Electricity Demand Forecast for Thailand Demand Side
Management Center. Information Technology Journal, 10(2), 32 - 42. (in Thai)
Sujjaviriyasup, T. (2018). Artificial Neural Network Model for Forecasting Monthly Price of Maize in Thailand.
SWU Sci.J, 34(1), 91-107. (in Thai)
Sutthison, T. (2019). Appropriate Models for Forecasting of Water Supply Consumption in Ubonratchathani
Province. Burapha Science Journal, 25(1), 28 – 50. (in Thai)
World Bank. (2021). Agriculture, forestry, and fishing, value added (% of GDP) – Thailand. December
10, 2021, from https://data.worldbank.org/indicator/NV.AGR.TOTL.ZS?locations=
TH&most_recent_year_desc=false
Xu, S., Chan, H. K., & Zhang, T. (2019). Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach. Transportation Research Part E: Logistics and Transportation Review, 122, 169 –180.
Xu, X., & Zhang, Y. (2021). Corn cash price forecasting with neural networks. Computers and Electronics in Agriculture, 184, 1-13
Yang, S., Chen, D., Li, S., & Wang, W. (2020). Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm. Science of The Total Environment, 716, 1-13.
Zhang, P.G. (2003). Time series forecasting using a hybrid ARIMA and neural network model.
Neurocomputing, 50,159 -175.
Zhu, Q., Zhang, F., Liu, S., Wu, Y., & Wang, L. (2019). A hybrid VMD–BiGRU model for rubber futures time series forecasting. Applied Soft Computing, 84, 1-12.
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Published
2023-01-04
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