Comparison of neural network NARX and NARMAX models for multi-step prediction using simulated and experimental data

Abstract

This paper provides the first extensive comparison of NARX and NARMAX models for multi-step prediction. NARMAX models are more complex and require more sophisticated training procedures than NARX models, as described in this paper. However, for some types of data, the NARMAX models provide significantly improved forecasts for shorter prediction horizons, although the predictions converge as the horizon increases. This paper analyzes the NARX and NARMAX predictors in depth on simulated and experimental data to determine those situations where the NARMAX predictor is preferred.