Time series forecasting model with error correction by structure adaptive RBF neural network

A hybrid methodology is proposed to take advantage of the unique strength of Autoregressive Integrated Moving Average (ARIMA) and RBF (Radial Basis Function) neural networks in linear and nonlinear modeling, which is an error correction method to create synergies in the overall forecasting process. ARIMA model is used to generate a linear forecast in the first stage, and then RBFN is developed as the nonlinear pattern recognition to correct the estimation error in ARIMA forecast. A dynamic clustering algorithm is developed to optimize the network structure, which makes the RBFN adapt to the specified training set, reduces computation complexity and avoids overfitting. With two real datasets, in terms of forecasting accuracy, empirical results evidently show that the hybrid model outperforms noticeably ARIMA and RBFN model used in isolation.