Time Series Forecasting Based on Novel Support Vector Machine Using Artificial Fish Swarm Algorithm

Abstract

Time series analysis is an important and complex problem in machine learning. Support vector machine (SVM) has recently emerged as a powerful technique for solving problems in regression, but its performance mainly depends on the parameters selection of it. Parameters selection for SVM is very complex in nature and quite hard to solve by conventional optimization techniques, which constrains its application to some degree. In this paper, artificial fish swarm algorithm (AFSA) is proposed to choose the parameters of least squares support vector machine (LS-SVM) automatically in time series prediction. This method has been applied in a real Electricity Load Forecasting, the results show that the proposed approach has a better generalization performance and is also more accurate and effective than LS-SVM based on particle swarm optimization.