A Short-Term Forecasting Algorithm for Network Traffic Based on Chaos Theory and SVM
Tóm tắt
Từ khóa
Tài liệu tham khảo
Papadopouli, M., Shen, H., Raftopoulos, E., Ploumidis, M., Hernandez-Campos, F.: Short-term traffic forecasting in a campus-wide wireless network. In: International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC’05), pp. 1446–1452. Berlin, Germany (2005)
Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting and Control. Prentice Hall, Englewood Cliffs (1999)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Lv, J.H., Lu, J.A., Chen, S.H.: Chaotic Time Series Analysis and Its Application. Wuhan University Press, Motto (2002). (in Chinese)
Huang, S.J., Shih, K.R.: Short-term load forecasting via ARMA model identification including non-gaussian process considerations. IEEE Trans. Power Syst. 18(2), 673–679 (2003)
Papagiannaki, K., Taft, N., Zhang, Z.L., Diot, C.: Long-term forecasting of internet backbone traffic. IEEE Trans. Neural Netw. 16(5), 1110–1124 (2005)
Abdennour, A.: Traffic prediction using neural networks evaluation of neural network architectures for MPEG-4 video traffic prediction. IEEE Trans. Broadcast. 52(2), 184–192 (2006)
Auld, T., Moore, A.W., Gull, S.F.: Bayesian neural networks for internet traffic classification. IEEE Trans. Neural Netw. 18(1), 223–239 (2007)
Chang, B.R., Tsai, H.F.: Novel hybrid approach to data-packet-flow prediction for improving network traffic analysis. Appl. Soft Comput. 9(3), 1177–1183 (2009)
Feng, H.F., Shu, Y.T., Wang, S.Y., Ma, M.D.: SVM-based models for predicting WLAN traffic. In: Proceedings of International Conference on Communications (ICC’06), pp. 597–602. Istanbul, Turkey (2006)
Zhang, W., Wu, Z.M., Yang, G.K.: Chaotic network attractor in packet traffic series. Comput. Phys. Commun. 161(3), 129–142 (2004)
Li, G.H., Zhu, C.M., Li, X.: Application of chaos theory and wavelet to modeling the traffic of wireless sensor networks. In: Proceedings of International Conference on Biomedical Engineering and Computer Science (ICBECS’2010), pp. 1–4. Wuhan, China (2010)
Lei, T., Yu, Z.W.: The local-model of chaos network traffic based on mutual information and principal componential analysis. In: Proceedings of Chinese Control and Decision Conference (CCDC’08), pp. 2762–2767. Yantai, China (2008)
Zhang, J.S., Dang, J.L., Li, H.C.: Local support vector machine forecasting of spatiotemporal chaotic time series. Acta Phys. Sin. 56(3), 67–77 (2007). (in Chinese)
Li, H.C., Hong, W., Wu, Y.R., Xu, S.J.: Research of chaos theory and local support vector machine in effective prediction of VBR MPEG video traffic. In: Proceedings of International Conference on Intelligent Computing (ICIC’06), pp. 1229–1234. Kunming, China (2006)
Xie, M., Liu, X.W., Zhang, J.: A novel IP traffic prediction method of chaos theory with support vector regression. In: Proceedings of International Symposium on Intelligent Information Technology Application (IITA’08), pp. 3–7. Shanghai, China (2008)
Takens, F.: Detecting strange attractors in fluid turbulence. In: Rand, D., Young, L.S. (eds.) Dynamical Systems and Turbulence, pp. 366–381. Springer, Warwick (1981)
Grassberger, P., Procaccia, I.: Dimension and entropy of strange attractors from a fluctuating dynamics approach. Phys. D. 13(1–2), 34–54 (1984)
Cao, L.Y.: Practical method for determining the minimum embedding dimension of a scalar time series. Phys. D. 110(1–2), 43–50 (1997)
Scholkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12(5), 1207–1245 (2000)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Wang, H.F., Hu, D.J.: Comparison of SVM and LS-SVM for regression. In: Proceedings of International Conference on Neural Networks and Brain (ICNNB’05), pp. 279–283. Beijing, China (2005)
Papadopouli, M., Raftopoulos, E., Shen, H.: Evaluation of short-term traffic forecasting algorithms in wireless networks. In: Proceedings of Next Generation Internet Design and Engineering (NGI’06), pp. 102–109. Valencia, Spain (2006)
Kruskall, J.B., Liberman, M.: The symmetric time warping problem: from continuous to discrete. In: Sankoff, D., Kruskal, J. (eds.) Time Warps, String Edits and Macromolecules: The Theory and Practice of Sequence Comparison, pp. 125–161. CSLI, Stanford (1999)
Salvador, S., Chan, P.: FastDTW: toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)
Kotz, D., Essien, K.: Analysis of a campus-wide wireless network. Wirel. Netw. 11(1–2), 115–133 (2005)
Crovella, M.E., Bestavros, A.: Self-similarity in World Wide Web traffic: evidence and possible causes. IEEE ACM Trans. Netw. 5(6), 835–846 (1997)