NARMAX-MODEL-BASED TIME SERIES PREDICTION: FEEDFORWARD AND RECURRENT FUZZY NEURAL NETWORK APPROACHES
Yang Gao, M.J. Er, Junping Du · 2004
The nonlinear autoregressive moving average with exogenous inputs (NARMAX) model provides a powerful representation for time series analysis, modeling and prediction due to its capability of accommodating the dynamic, complex and nonlinear nature of real-world time series prediction problems. This paper focuses on the modeling and prediction of NARMAX-model-based time series using the fuzzy neural network (FNN) methodology. Both feedforward and recurrent FNNs approaches are proposed. Experiments and comparative studies demonstrate that the proposed FNN approaches can effectively learn complex temporal sequences in an adaptive way.