A Novel FRBF-Type Model for Nonlinear Time Series Prediction

Accurate prediction of time series is complex due to nonlinear characteristics but can play a significant role in practical problem. In this paper, a novel varying-coefficient hybrid model is proposed to accurately predict the nonlinear time series. A set of fuzzy radial basis function (FRBF) neural networks is used to approximate the varying functional coefficients of the state-dependent autoregressive model with exogenous variables (SD-ARX). The obtained model is called the fuzzy radial basis function network-based autoregressive model with exogenous variables (FRBF-ARX), which combines the advantages of the FRBF in function approximation and the SD-ARX model in nonlinear dynamics description. Then, a structured nonlinear parameter optimization method (SNPOM) and the modified multifold cross-validation criterion are used to estimate the parameters of the proposed varying-coefficient FRBF-ARX model. The performances of the FRBF-ARX model are used to predict the PM2.5 concentration and simulated SISO nonlinear process, respectively, and the performances of the proposed model are also compared and discussed. The experimental results show that the FRBF-ARX model has better performances of accuracy on nonlinear time series forecasting than that of other models.