Multiscale bilinear recurrent neural networks and their application to the long-term prediction of network traffic

Authors:
Dong-Chul Park;Chung Nguyen Tran;Yunsik Lee
Affiliations:
ICRL, Dept. of Information Engineering, Myong Ji University, Korea;ICRL, Dept. of Information Engineering, Myong Ji University, Korea;SoC Research Center, Korea Electronics Tech. Inst., Seongnam, Korea
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

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Abstract

A new wavelet-based neural network architecture employing the BiLinear Recurrent Neural Network (BLRNN) for time-series prediction is proposed in this paper. It is called the Multiscale BiLinear Recurrent Neural Network (M-BLRNN). The wavelet transform is employed to decompose the time-series to a multiresolution representation while the BLRNN model is used to predict a signal at each level of resolution. The proposed M-BLRNN algorithm is applied to the long-term prediction of network traffic. The performance of the proposed M-BLRNN algorithm is evaluated and compared with the traditional MultiLayer Perceptron Type Neural Network (MLPNN) and the BLRNN. The results show that the M-BLRNN gives a 20.8% to 76.5% reduction in terms of the normalized mean square error (NMSE) over the MLPNN and the BLRNN.