Time Series Forecasting for Economic Growth Based on Particle Swarm Optimization and Support Vector Machine

Abstract

Economic growth forecasting is important to make the policy on national economic development. Support vector machine (SVM) is a new machine learning method, which seeks to minimize an upper bound of the generalization error instead of the empirical error as in conventional neural networks. In the study, support vector machine and particle swarm optimization is applied in economic growth forecasting, PSO is to find the optimal settings of parameters in SVM. The total output value of Xi'an city from 1990 to 2000 was employed to compare the forecasting performances of the proposed PSVM model and RBF neural network forecasting model in economic growth forecasting. The experiment results indicate that the proposed hybrid PSOSVM algorithm is better than the RBFNN in economic growth forecasting.