Long-term time series prediction with the NARX network: An empirical evaluation

Abstract

The NARX network is a dynamical neural architecture commonly used for input–output modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward time delay neural network (TDNN), i.e., without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, we show that the original architecture of the NARX network can be easily and efficiently applied to long-term (multi-step-ahead) prediction of univariate time series. We evaluate the proposed approach using two real-world data sets, namely the well-known chaotic laser time series and a variable bit rate (VBR) video traffic time series. All the results show that the proposed approach consistently outperforms standard neural network based predictors, such as the TDNN and Elman architectures.