Track: Ph.D. Thesis/Dissertation Presentation

Abstract

The objective of this research is to construct a Thailand’s Para rubber production forecasting model. It will be advantageous to farmers, entrepreneurs and other organizations for the right planning and decision making in order to prepare themselves to be ready for the modernized global economics trends which will affect to Thailand’s agricultural economy. Four forecasting techniques used in this research artificial neural network (ANN), particle swarm optimization algorithm (PSO), support vector machine (SVM) and hybrid model PSO&SVM. The mean absolute percentage error is used to identify the most appropriate model. The results of the research show that the hybrid PSO&SVM model obtains the lowest mean absolute percentage error of 0.0040%, while the particle swarm optimization model, support vector machine model and artificial neural network model have mean absolute percentage error of 0.0388%, 0.0388% and 0.0414%  respectively.