Enhancing Accuracy in Predicting Thailand's Rice Exports: A Hybrid Modeling Approach
Keywords:Hybrid model, Empirical mode decomposition, Support vector regression, Genetic algorithm, SARIMA model
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
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