Enhancing Accuracy in Predicting Thailand's Rice Exports: A Hybrid Modeling Approach
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Published
Nov 16, 2023
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Abstract
Thailand's rice exports are currently experiencing a declining trend in relation to the proportion of rice production. This calls for the need to accurately predict future developments, which holds immense importance for stakeholders involved. Accurate predictions enable the formulation of effective policies and strategies to boost Thailand's rice exports in the future. To address this objective, this research aims to identify a suitable model for forecasting the monthly quantity of Thailand's rice exports. A sophisticated hybrid model is proposed, integrating the strengths of Empirical Mode Decomposition (EMD), Seasonal Auto-Regressive Integrated Moving Average (SARIMA), and Support Vector Regression (SVR). The model's parameters are optimized using Genetic Algorithm (GA) to ensure optimal performance. To evaluate the hybrid model's effectiveness, rigorous performance criteria are employed, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). These metrics provide a comprehensive assessment of the model's predictive capabilities and overall performance. The research findings demonstrate that the developed hybrid model outperforms individual models across all performance criteria. This solidifies its reliability in generating accurate forecasts for Thailand's monthly rice export quantities. Consequently, the hybrid model emerges as a valuable tool for organizations seeking to proactively forecast and effectively manage the dynamics of Thailand's rice exports in the future.
Keywords: Hybrid model, Empirical mode decomposition, Support vector regression, Genetic algorithm, SARIMA model
References
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Zhang, Z., Ding, S., & Sun, Y. (2020). A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task. Neurocomputing, 410, 185–201. https://doi.org/10.1016/j.neucom.2020.05.075
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2008). Time Series Analysis: Forecasting and Control (4th ed.). New Jersey: John Wiley & Sons.
Chen, W., Xu, H., Chen, Z., & Jiang, M. (2021). A novel method for time series prediction based on error decomposition and nonlinear combination of forecasters. Neurocomputing, 426, 85–103. https://doi.
org/10.1016/j.neucom.2020.10.048
Co, H. C., & Boosarawongse, R. (2007). Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks. Computers & Industrial Engineering, 53, 610–627. https://doi.org/10.
1016/j.cie.2007.06.005
Duan, W. Y., Han, Y., Huang, L. M., Zhao, B. B., & Wang, M. H. (2016). A hybrid EMD-SVR model for the short-term prediction of significant wave height. Ocean Engineering, 124, 54–73. https://doi.
org/10.1016/j.oceaneng.2016.05.049
Fan, G. F., Yu, M., Dong, S. Q., Yeh, Y. H., & Hong, W. C. (2021). Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling. Utilities Policy, 73, 1-18. https://doi.org/10.1016/j.jup.2021.101294
Feng, Z., Niu, W., Tang, Z., Jiang, Z., Xu, Y., Liu, Y., & Zhang, H. (2020). Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. Journal of Hydrology, 583, 1-12. https://doi.org/10.1016/j.
jhydrol.2020.124627
Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation. Departmental Technical Report, 2, 1-6.
Gu, Y. H., Yoo, S. J., Park, C. J., Kim, Y. H., Park, S. K., Kim, J. S., & Lim, J. H. (2016). BLITE-SVR: New forecasting model for late blight on potato using support-vector regression. Computers and Electronics in Agriculture, 130, 169–176. https://doi.org/10.1016/j.compag.2016.10.005
Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology Control and Artificial Intelligence. Ney York: University of Michigan Press.
Huang, N.E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N. C., Tung, C. C., & Liu, H.H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis, Royal Society, 454, 903–995. https://doi.org/10.1098/
rspa.1998.0193
Huang, Y., Hasan, N., Deng, C., & Bao, Y. (2021). Multivariate Empirical Mode Decomposition based Hybrid Model for Day-ahead Peak Load Forecasting. Energy, 239, 1-15. https://doi.org/10.1016/j.
energy.2021.122245
Jeong, S., Ko, J., & Yeom, J. (2022). Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea. Science of the Total Environment, 802, 1-12. https://doi.org/10.1016/j.scitotenv.2021.149726
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, 1-19. https://doi.org/10.
3390/math8101722
Keerativibool, W. (2014). Forecasting Model for the Export Value of Thai Jasmine Rice. Burapha Science Journal, 19, 78-90.
Liu, L.W., Ma, X., Wang, Y. M., Lu, C. T., & Lin, W. S. (2021). Using artificial intelligence algorithms to predict rice (Oryza sativa L.) growth rate for precision agriculture. Computers and Electronics in Agriculture, 187, 1-8. https://doi.org/10.1016/j.compag.2021.106286
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C. C., & Lin, C. C. (2015). Misc Functions of the Department of Statistics. Retrieved from http://cran.r-project.org/web/
packages/e1071/index.html
Meng, E., Huang, S., Huang, Q., Fang, W., Wu, L., & Wang, L. (2019). A robust method for non-stationary streamflow prediction based on improved EMD-SVM model. Journal of Hydrology, 568, 462–478.
https://doi.org/10.1016/j.jhydrol.2018.11.015
Mi, X., Liu, H., & Li, Y. (2019). Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine. Energy Conversion and Management, 180, 196–205. https://doi.org/10.1016/j.enconman.2018.11.006
Office of Agricultural Economics. (2022). Export. Retrieved from http://impexp.oae.go.th/service/export.php?S
Prado, F., Minutolo, M. C., & Kristjanpoller, W. (2020). Forecasting based on an ensemble Autoregressive Moving Average—Adaptive neuro—Fuzzy inference system – Neural network—Genetic Algorithm Framework. Energy, 197, 1-13. https://doi.org/10.1016/j.energy.2020.117159
Qiu, X., Ren, Y., Suganthan, P. N., & Amaratunga, G. A. J. (2017). Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting. Applied Soft Computing, 54, 246–255. https://doi.org/10.1016/j.asoc.2017.01.015
R Core Team. (2021). forecast: Forecasting Functions for Time Serie s and Linear Models. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://cran.r-project.org/web/packages/
forecast/index.html
Scrucca, L. (2013). GA: A Package for Genetic Algorithms in R. Journal of Statistical Software, 53(4), 1–37. https://doi.org/10.18637/jss.v053.i04
Su, Y., Xu, H., & Yan, L. (2017). Support vector machine-based open crop model (SBOCM): Case of rice production in China. Saudi Journal of Biological Sciences, 24, 537–547. https://doi.org/10.1016/j.
sjbs.2017.01.024
Sujjaviriyasup, T. (2021). A hybridization of feedforward neural network and differential evolution to forecast fertilizer consumption emphasizing on selecting optimal architecture. Songklanakarin Journal of Science and Technology, 43, 1160–1168.
Tang, L. H., Bai, Y. L., Yang, J., & Lu, Y. N. (2020). A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series. Chaos, Solitons & Fractals, 141, 1-12. https://doi.org/10.1016/j.chaos.2020.110366
Vapnik, V. N. (1998). The Nature of Statistical Learning Theory. New York: Springer Verlag.
Willighagen, E., & Ballings, M. (2021). R Based Genetic Algorithm. CRAN Repository. Retrieved from https://cran.r-project.org/web/packages/genalg/
Xu, X., & Zhang, Y. (2021). Corn cash price forecasting with neural networks. Computers and Electronics in Agriculture, 184, 1-13. https://doi.org/10.1016/j.compag.2021.106120
Yang, Y., Che, J., Deng, C., & Li, L. (2019). Sequential grid approach-based support vector regression for short-term electric load forecasting. Applied Energy, 238, 1010–1021. https://doi.org/10.1016/j.
apenergy.2019.01.127
Yaslan, Y., & Bican, B. (2017). Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting. Measurement, 103, 52–61. http://dx.doi.org/10.1016/j.measurement.2017.02.007
Zhang, Z., Ding, S., & Sun, Y. (2020). A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task. Neurocomputing, 410, 185–201. https://doi.org/10.1016/j.neucom.2020.05.075
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How to Cite
VONGLAO, Paothai et al.
Enhancing Accuracy in Predicting Thailand's Rice Exports: A Hybrid Modeling Approach.
Naresuan University Journal: Science and Technology (NUJST), [S.l.], v. 31, n. 4, p. 1-21, nov. 2023.
ISSN 2539-553X.
Available at: <https://www.journal.nu.ac.th/NUJST/article/view/3441>. Date accessed: 29 apr. 2024.
doi: https://doi.org/10.14456/nujst.2023.31.