Time series study of GGAP-RBF network: predictions of Nasdaq stock and nitrate contamination of drinking water

This paper investigates the performance of the latest developed GGAP-RBF network in time series prediction applications. The growing and pruning strategy of GGAP-RBF are based on linking the required learning accuracy with the significance of the nearest added new neuron. Significance of a neuron is a measure of the average information content of that neuron. GGAP-RBF algorithm may be attractive in real time-series applications due to its good efficiency and simple topology. This paper investigates its performance in two important real time-series applications: predictions of Nasdaq stock and weekly nitrate contamination of drinking water. The simulation results demonstrate that GGAP-RBF network can achieve good prediction accuracy in an efficient and easy way.