Enhanced Echo-State Restricted Boltzmann Machines for Network Traffic Prediction

Abstract

Network traffic prediction is a great challenge due to complex statistical properties, generally covering the long-range correlations and self-similarity. To address this issue, this article applies an integrated neural computing model to predict network traffic, namely, enhanced echo-state restricted Boltzmann machine (eERBM). In structure, this model possesses the following functional components of feature learning, information compensation, input superposition, and supervised nonlinear approximation. It is motivated by the introduction of information theory in modeling the hybrid architecture of the echo state network and the restricted Boltzmann machine. This is the first attempt that eERBM is applied in network traffic prediction tasks of different origin and characteristics, considering TCP/IP packet and variable-bit-rate video. By performing a theoretical analysis, we show that eERBM achieves superior nonlinear approximation and robustness in comparison to the baseline methods, and effectively preserves the self-similarity of network traffic traces.