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.
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Lee, MW., Park, DC., Lee, Y. (2007). Multiscale BiLinear Recurrent Neural Network for Prediction of MPEG Video Traffic. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_15
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DOI: https://doi.org/10.1007/978-3-540-71701-0_15
Publisher Name: Springer, Berlin, Heidelberg
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