A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting

Abstract

Considering that in the literature there is a very limited number of studies proposing new SVM kernels especially in regression problems, the scope of this research is to investigate the development of a novel Support Vector Machine Kernel. The proposed new W-SVM (Weighted-SVM) kernel was developed by applying a suitably transformed weight vector derived from particle swarm optimized neural networks in order to satisfy the kernel conditions of Mercer’s theorem and then incorporated to a Bayesian Optimized (BO) kernel for building the new proposed W-SVM kernel. The proposed SVM kernel was applied in Gross Domestic Product growth forecasting. The new kernel has led to significantly improved forecasting results compared to all the other conventional ANN, SVM, and optimized BO-SVM, PSO-ANN machine learning models.