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.
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References
Suzuki, Y.: Prediction of Daily Traffic Volumes by Using Autoregressive Models. Proc. IEEE Conf. on Vehicular Technology 3, 2103–2107 (2000)
Basu, S., Mukherjee, A., Klivansky, S.: Time Series Models for Internet Traffic. IEEE Joint Conf. on Computer Societies 2, 611–620 (1996)
Connor, J., Martin, R., Altas, L.: Recurrent Neural Networks and Robust Time Series Prediction. IEEE Trans. on Neural Networks 5(2), 240–254 (1994)
Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: the Wavelet Representation. IEEE Trans. Pattern Anal. Machine Intell. 11(7), 674–693 (1989)
Park, D.C., Zhu, Y.: Bilinear Recurrent Neural Network. IEEE ICNN 3, 1459–1464 (1994)
Park, D.C., Jeong, T.K.: Complex Bilinear Recurrent Neural Network for Equalization of a Satellite Channel. IEEE Trans. on Neural Networks 13(3), 711–725 (2002)
Park, D.C., Nguyen, D.H., Hong, S.J., Lee, Y.: Equalization of a Wireless ATM Channel with Simplified Complex BiLinear Reccurent Neural Network. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 1113–1116. Springer, Heidelberg (2005)
Shensa, M.J.: The Discrete Wavelet Transform: Wedding the Trous and Mallat Algorithms. IEEE Trans. on Signal Proc. 40(10), 2463–2482 (1992)
Leland, W.E., Wilson, D.V.: High Time-Resolution Measurement and Analysis of LAN Traffic: Implications for LAN Interconnection. In: Proc. IEEE INFOCOM, pp. 1360–1366 (1991)
Fowler, H.J., Leland, W.E.: Local Area Network Traffic Characteristics, with Implications for Broadband Network Congestion Management. IEEE JSAC, 1139–1149 (1991)
Alarcon-Aquino, V., Barria, J.A.: Multiresolution FIR Neural-Network-Based Learning Algorithm Applied to Network Traffic Prediction. IEEE Trans. on Sys. Man. and Cyber. PP(99), 1–13 (2005)
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Park, DC., Tran, C.N., Lee, Y. (2006). Multiscale BiLinear Recurrent Neural Networks and Their Application to the Long-Term Prediction of Network Traffic. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_29
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DOI: https://doi.org/10.1007/11760191_29
Publisher Name: Springer, Berlin, Heidelberg
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