Using PSO algorithm to evolve an optimum input subset for a SVM in time series forecasting

Using particle swarm optimization (PSO) algorithm to evolve an optimum input subset for a SVM is proposed Binary PSO algorithm is employed in feature selection, in which each particle represented as a binary vector corresponds to a candidate input subset. A swarm of particles flies through the input set space for targeting the optimal subset. In order to evaluate the reasonable fitness of each input subset, PSO algorithm is used to adoptively evolve SVM to obtain the best performance of network, in which each particle represented as a real vector corresponds to the candidate kernel parameters of SVM. This method has been applied in a real financial time series forecasting, the results show that it has better performance of generalization, and higher rate of convergence.