A New Look at Nonlinear Time Series Prediction with NARX Recurrent Neural Network

Authors:
Jose M. P. Menezes Jr.;Guilherme A. Barreto
Affiliations:
Federal University of Ceara, Brazil;Federal University of Ceara, Brazil
Venue:
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Year:
2006

Citing 0
Cited 2

A hybrid of nonlinear autoregressive model with exogenous input and autoregressive moving average model for long-term machine state forecasting

Expert Systems with Applications: An International Journal
Management and forecast of dynamic customer needs: An artificial immune and neural system approach

Advanced Engineering Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The NARX network is a recurrent neural architecture commonly used for input-output modeling of nonlinear systems. The input of the NARX network is formed by two tapped-delay lines, one sliding over the input signal and the other one over the output signal. Currently, when applied to chaotic time series prediction, the NARX architecture is designed as a plain Focused Time Delay Neural Network (FTDNN); thus, limiting its predictive abilities. In this paper, we propose a strategy that allows the original architecture of the NARX network to fully explore its computational power to improve prediction performance. We use the well-known chaotic laser time series to evaluate the proposed approach in multi-step-ahead prediction tasks. The results show that the proposed approach consistently outperforms standard neural network based predictors, such as the FTDNN and Elman architectures.