Economic Growth Prediction Using Optimized Support Vector Machines

Without a doubt the GDP1 is one of the substantial indicators in evaluation of every nation’s economic growth. There are different types of econometric parameters that affect on the behavior of GDP and make it excessively non-linear and high- stochastic. Under such circumstances, experts are being tried in the development of techniques in order to define such a complex phenomenon. The main objective of this research is to propose a hybrid model to predict the future GDP of Turkey. The proposed model consists of three stages. In the first stage, after lag selection, the most efficient features are selected using SRA2. Afterward, these variables are used in order to develop proposed model, in which the model uses support vector machines that the parameters of which are tuned by GA3. Finally, results demonstrated that accuracy of the proposed hybrid model is highly promising than the ANN4 and ANFIS5 models.

Keywords:

GDP , Stepwise Regression Algorithm , Support Vector Machines , Genetic Algorithms.