On recurrent neural networks for auto-similar traffic prediction: A performance evaluation

Abstract

The NARX network is a recurrent neural architecture commonly used for input-output modelling 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 nonlinear 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 NARX architecture to fully exploit its computational resources to improve prediction performance. We use real-world VBR video traffic 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 Flman architectures.