Nonlinear Autoregressive Moving-average (NARMA) Time Series Forecasting Using Neural Networks

摘要

In this paper, a one-step forecasting comparison using a simulated nonlinear autoregressive moving-average time series (NARMA) was conducted between two groups of neural networks. Group I is neural networks that use only autoregressive inputs, while Group II is neural networks that use autoregressive and moving-average (i.e., error feedback) inputs. Simulation results showed that the models in Group II produce more accurate forecasts as compared to the models in Group I. That means, introducing error feedback to neural networks helps in forecasting NARMA time series. Another comparison was conducted between autoregressive moving-average (ARMA) model and neural network models using the simulated NARMA time series. As expected, since it is a nonlinear time series, neural networks show better results as compared to ARMA model.