Time-series forecasting using GA-tuned radial basis functions

Abstract

Abstract In this paper we provide a nonlinear auto-regressive (NAR) time-series model for forecasting applications. The nonlinearity is introduced by using radial basis functions. RBF networks are widely used in time-series analysis. Three main parameter sets are involved in RBF learning process. They are the centers and widths of the radial functions, and their weights. Although the selection of the RBF centers and widths is important, most reported research has dealt only with the problem of weight optimization by making assumptions about the centers and widths. Therefore, there is no guarantee for finding the global optimum with respect to all sets of parameters. In this paper we use genetic algorithms (GAs) to simultaneously optimize all of the RBF parameters so that an effective time-series is designed and used for forecasting. An example is presented with promising results.