Chinese Grain Production Forecasting Method Based on Particle Swarm Optimization-based Support Vector Machine

The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network in Chinese grain production forecasting, and is a proper method in Chinesegrain production forecasting.

Abstract

Forecasting of grain production is an important resource for establishing agriculture policy. Particle swarm optimization-based support vector machine (PSO-SVM) is applied to forecast grain production in this paper. In PSOSVM model, particle swarm optimization (PSO) is used to determine free parameters of support vector machine. PSO is a new optimization method, which is motivated by social behavior of bird flocking or fish schooling. The optimization method not only has strong global search capability, but also is very easy to implement. The Chinese grain production is used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the PSO-SVM method can achieve greater forecasting accuracy than grey model, artificial neural network in Chinese grain production forecasting. Consequently, PSO-SVM is a proper method in Chinese grain production forecasting. Keywords: Grain production forecasting, support vector machine, particle swarm optimization, time series forecasting, parameters optimization