Multiscale BiLinear Recurrent Neural Networks and Their Application to the Long-Term Prediction of Network Traffic

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 m...

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Bibliographic Details
Published inAdvances in Neural Networks - ISNN 2006 pp. 196 - 201
Main Authors Park, Dong-Chul, Tran, Chung Nguyen, Lee, Yunsik
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Normalize Mean Square Error
Original Signal
Prediction Performance
Recurrent Neural Network
Resolution Level
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Summary: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.
ISBN:9783540344827
3540344829
ISSN:0302-9743
1611-3349
DOI:10.1007/11760191_29