A new hybrid evolutionary based RBF networks method for forecasting time series: A case study of forecasting emergency supply demand time series

• This paper presents a new hybrid evolutionary based RBF networks method for forecasting time series.
• A new hybrid evolutionary algorithm is developed to determine both architecture and network parameters of radial basis functions neural networks simultaneously.
• The applicability and capability are demonstrated for several existing benchmark time series modeling and algorithms.
• The proposed method is applied for forecasting emergency supply demand time series.

Improving time series forecasting accuracy has received considerable attention in recent years. This paper presents a new hybrid evolutionary algorithm for determining both architecture (input variables and neurons of hidden layer) and network parameters (centers, width and weights) of radial basis function neural networks (RBFNNs) simultaneously. Our proposed algorithm generates new architecture applying genetic algorithm (GA). Modified adaptive particle swarm optimization (APSO) is used to determine the training parameters efficiently. Inertia weight and acceleration coefficients in APSO are adapted by swarm status. Since PSO algorithms suffer premature convergence, especially when global best is found, mutation operator is applied to overcome the drawback. Comparing the performance of the proposed approach with several benchmark time series modeling and algorithms shows that the proposed method is able to predict time series more accurately than others. Finally, proposed GA–APSO based RBFNNs method is applied to predict the demand of emergency supplies after earthquake in the East Azerbayjan in 2012 in Iran. The results show that the proposed evolving RBF based method can be applied to forecast the emergency supply demand time series successfully with the automatically selected nodes and inputs.