Multiscale bilinear recurrent neural network for prediction of MPEG video traffic

Authors:
Min-Woo Lee;Dong-Chul Park;Yunsik Lee
Affiliations:
Dept. of Information Eng., and Myongji IT Eng. Research Inst., Myong Ji University, Korea;Dept. of Information Eng., and Myongji IT Eng. Research Inst., Myong Ji University, Korea;SoC Research Center, Korea Electronics Tech. Inst., Seongnam, Korea
Venue:
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2007

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Abstract

A MPEG video traffic prediction model in ATM networks using the Multiscale BiLinear Recurrent Neural Network (M-BLRNN) is proposed in this paper. The M-BLRNN is a wavelet-based neural network architecture based on the BiLinear Recurrent Neural Network (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-based predictor is applied to real-time MPEG video traffic data. When compared with the MLPNN-based predictor and the BLRNN-based predictor, the proposed M-BLRNN-based predictor shows 16%-47% improvement in terms of the Normalized Mean Square Error (NMSE) criterion.