Hybrid Model of Linear and Nonlinear Models for Forecasting Annual Molumes of International Rice Exports
Abstract
In this article, a hybrid model of ARIMA and SVM models is developed to forecast three rice volumes of exporters, Thailand, India, and Viet Nam, which are major exporters of international rice market. The ARIMA model is used to prominently describe time series data with linear component while the SVM model is suitable for building complex function in order to predict nonlinear component. In addition, the hybrid model is compared with three traditional forecasting models, Naïve, ARIMA, and SVM, based on five accuracy measures which are Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, Median Absolute Percentage Error and Symmetric Mean Absolute Percentage Error. The empirical results indicate that the hybrid model outperforms three traditional models based on all accuracy measures. Consequently, the hybrid model is used to be a tool for predicting the three rice volumes of exporters to support a decision making of rice cultivation in each season. Keywords : Hybrid model, ARIMA model, Support vector machine model, export, riceReferences
Arunmas, P. (2017). Thai rice: Future brightens after over-supply crisis passes. Retrieved September 3, 2018,
from https://www.bangkokpost.com/learning/advanced/1272265/thai-rice-future-brightens-after-over
supply-crisis-passes.
Balakrishnan, N., & Muthukumarasamy, G. (2016). Crop production-ensemble machine learning model for
prediction. International Journal of Computer Science and Software Engineering, 5(7), 148.
Bangkokbiznews. (2018). Five economic crops tend to increase 3.2% in 2018. Retrieved September 3, 2018,
from http://www.bangkokbiznews.com/news/detail/795970.
Boosarawongse, R. (2006). AN APPLICATION OF BOX-JENKINS METHOD IN THE FORECASTING OF QUANTITY
OF THAI RICE EXPORT. In Proceedings of the 11th Annual Conference of Asia Pacific Decision
Sciences Institute.
Co, H. C. & Boosarawongse, R. (2007). Forecasting Thailand’s rice export: Statistical techniques vs. artificial
neural networks. Computers & industrial engineering, 53(4), 610-627.
Food and Agriculture Organization of the United Nations. (2018). Crops and livestock products. Retrieved August
3, 2018, from http://www.fao.org/faostat/en/#data/TP.
Gandhi, N., Armstrong, L. J., Petkar, O., & Tripathy, A. K. (2016, July). Rice crop yield prediction in India using
support vector machines. In Computer Science and Software Engineering (JCSSE), 2016 13th
International Joint Conference on (pp. 1-5). IEEE.
Hyndman, R., J., and Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R.
Journal of Statistical Software, 26(3).
Jha, G. K., & Sinha, K. (2013). Agricultural Price Forecasting Using Neural Network Model: An Innovative
Information Delivery System. Agricultural Economics Research Review, 26(2).
Mitra, D., & Paul, R. K. (2017). Hybrid time-series models for forecasting agricultural commodity prices. Model
Assisted Statistics and Applications, 12(3), 255-264.
Naveena, K., Singh, S., Rathod, S., & Singh, A. (2017). Hybrid ARIMA-ANN Modelling for Forecasting the Price of
Robusta Coffee in India. Int. J. Curr. Microbiol. App. Sci, 6(7), 1721-1726.
Petchseechoung, W. (2017). THAILAND INDUSTRY OUTLOOK 2017-19: RICE INDUSTRY. Retrieved September
3, 2018, from https://www.krungsri.com/bank/getmedia/83a146ea-a14f-41c7-9e80-
9214a5d9b963/IO_Rice_201705_EN.aspx.
Pokora, J. (2017). Hybrid ARIMA and Support Vector Regression in Short-term Electricity Price Forecasting.
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 65(2), 699-708.
Rathod, S., & Mishra, G. C. (2018). Statistical Models for Forecasting Mango and Banana Yield of Karnataka,
India. Journal of Agricultural Science and Technology, 20,803-816.
Saicheua, S. (2016). Rice price fall I. Retrieved September 3, 2018, from
http://www.bangkokbiznews.com/blog/detail/639362.
Shabri, A., Samsudin, R., & Ismail, Z. (2009). Forecasting of the rice yields time series forecasting using artificial
neural network and statistical model. Journal of Applied Sciences, 9(23), 4168-4173.
Sujjaviriyasup, T., & Pitiruek, K. (2013a). Agricultural Product Forecasting Using Machine Learning Approach.
Int. Journal of Math. Analysis, 7(38), 1869-1875.
Sujjaviriyasup, T., & Pitiruek, K. (2013b). Hybrid ARIMA-support vector machine model for agricultural production
planning. Applied Mathematical Sciences, 7(57), 2833-2840.
Wen, Q., Wang, Y., Zhang, H., & Li, Z. (2018). Application of ARIMA and SVM mixed model in agricultural
management under the background of intellectual agriculture. Cluster Computing, 1-10.
Workman, D. (2018). Rice Exports by Country. Retrieved September 3, 2018, from
http://www.worldstopexports.com/rice-exports-country/.
from https://www.bangkokpost.com/learning/advanced/1272265/thai-rice-future-brightens-after-over
supply-crisis-passes.
Balakrishnan, N., & Muthukumarasamy, G. (2016). Crop production-ensemble machine learning model for
prediction. International Journal of Computer Science and Software Engineering, 5(7), 148.
Bangkokbiznews. (2018). Five economic crops tend to increase 3.2% in 2018. Retrieved September 3, 2018,
from http://www.bangkokbiznews.com/news/detail/795970.
Boosarawongse, R. (2006). AN APPLICATION OF BOX-JENKINS METHOD IN THE FORECASTING OF QUANTITY
OF THAI RICE EXPORT. In Proceedings of the 11th Annual Conference of Asia Pacific Decision
Sciences Institute.
Co, H. C. & Boosarawongse, R. (2007). Forecasting Thailand’s rice export: Statistical techniques vs. artificial
neural networks. Computers & industrial engineering, 53(4), 610-627.
Food and Agriculture Organization of the United Nations. (2018). Crops and livestock products. Retrieved August
3, 2018, from http://www.fao.org/faostat/en/#data/TP.
Gandhi, N., Armstrong, L. J., Petkar, O., & Tripathy, A. K. (2016, July). Rice crop yield prediction in India using
support vector machines. In Computer Science and Software Engineering (JCSSE), 2016 13th
International Joint Conference on (pp. 1-5). IEEE.
Hyndman, R., J., and Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R.
Journal of Statistical Software, 26(3).
Jha, G. K., & Sinha, K. (2013). Agricultural Price Forecasting Using Neural Network Model: An Innovative
Information Delivery System. Agricultural Economics Research Review, 26(2).
Mitra, D., & Paul, R. K. (2017). Hybrid time-series models for forecasting agricultural commodity prices. Model
Assisted Statistics and Applications, 12(3), 255-264.
Naveena, K., Singh, S., Rathod, S., & Singh, A. (2017). Hybrid ARIMA-ANN Modelling for Forecasting the Price of
Robusta Coffee in India. Int. J. Curr. Microbiol. App. Sci, 6(7), 1721-1726.
Petchseechoung, W. (2017). THAILAND INDUSTRY OUTLOOK 2017-19: RICE INDUSTRY. Retrieved September
3, 2018, from https://www.krungsri.com/bank/getmedia/83a146ea-a14f-41c7-9e80-
9214a5d9b963/IO_Rice_201705_EN.aspx.
Pokora, J. (2017). Hybrid ARIMA and Support Vector Regression in Short-term Electricity Price Forecasting.
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 65(2), 699-708.
Rathod, S., & Mishra, G. C. (2018). Statistical Models for Forecasting Mango and Banana Yield of Karnataka,
India. Journal of Agricultural Science and Technology, 20,803-816.
Saicheua, S. (2016). Rice price fall I. Retrieved September 3, 2018, from
http://www.bangkokbiznews.com/blog/detail/639362.
Shabri, A., Samsudin, R., & Ismail, Z. (2009). Forecasting of the rice yields time series forecasting using artificial
neural network and statistical model. Journal of Applied Sciences, 9(23), 4168-4173.
Sujjaviriyasup, T., & Pitiruek, K. (2013a). Agricultural Product Forecasting Using Machine Learning Approach.
Int. Journal of Math. Analysis, 7(38), 1869-1875.
Sujjaviriyasup, T., & Pitiruek, K. (2013b). Hybrid ARIMA-support vector machine model for agricultural production
planning. Applied Mathematical Sciences, 7(57), 2833-2840.
Wen, Q., Wang, Y., Zhang, H., & Li, Z. (2018). Application of ARIMA and SVM mixed model in agricultural
management under the background of intellectual agriculture. Cluster Computing, 1-10.
Workman, D. (2018). Rice Exports by Country. Retrieved September 3, 2018, from
http://www.worldstopexports.com/rice-exports-country/.
Downloads
Published
2019-05-14
Issue
Section
Research Article
