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 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.
KeywordsPrediction Performance Original Signal Recurrent Neural Network Resolution Level Normalize Mean Square Error
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