A new look at nonlinear time series prediction with NARX recurrent neural network

• Autor(a) principal: Meneses, José Wally Mendonça de
• Data de Publicação: 2006
• Outros Autores: Barreto, Guilherme de Alencar
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Texto Completo: http://www.repositorio.ufc.br/handle/riufc/70658

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.