Forecasting the Flow of Data Packets for Website Traffic Analysis – ASVR-Tuned ANFIS/NGARCH Approach

  • Bao Rong Chang
  • Shi-Huang Chen
  • Hsiu Fen Tsai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


Forecast of the flow of data packets between client and server for a website traffic analysis is viewed as a part of web analytics. Thousands of web-smart businesses depend on web analytics to improve website conversions, reduce marketing costs, website optimization, website monitoring and provide a higher level of service to their customers and partners. This paper particularly intends to develop a high-accuracy prediction approach as the need for a website traffic analysis. The proposed composite model (ASVR-ANFIS/NGARCH) is schemed to build a systematic structure such that it is not only to improve the predictive accuracy because of resolving the problems of the overshoot and volatility clustering simultaneously, but also to boost website tracking capacity helping each webmaster to optimize their website, maximize online marketing conversions and lead campaign tracking.


Data Packet Radial Basis Function Neural Network Composite Model Computer Center Grey Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Funkhouser, T.A., Sequin, C.H., Teller, S.J.: Management of Large Amounts of Data in Interactive Building Walkthroughs. In: Proc. ACM 0-89791-471-6/92/0003/0011 (1992)Google Scholar
  2. 2.
    Aissi, S., Malu, P., Srinivasan, K.: E-business Process Modeling: The Next Big Step. IEEE Computer 35(5), 55–62 (2002)Google Scholar
  3. 3.
    Chang, B.R.: Hybrid BPNN-Weighted Grey-CLMS Forecasting. Journal of Information Science and Engineering 21(1), 209–221 (2005)Google Scholar
  4. 4.
    Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis: Forecasting & Control. Prentice-Hall, New Jersey (1994)MATHGoogle Scholar
  5. 5.
    Haykin, S.: Neural Network: A Comprehensive Foundation, 2nd edn. Prentice Hall, New Jersey (1999)MATHGoogle Scholar
  6. 6.
    Jang, J.-S.R.: ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics 23(3), 665–685 (1993)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Gourieroux, C.: ARCH Models and Financial Applications. Springer, New York (1997)MATHGoogle Scholar
  8. 8.
    Bellerslve, T.: Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics 31, 307–327 (1986)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Chang, B.R.: Compensation and Regularization for Improving the Forecasting Accuracy by Adaptive Support Vector Regression. International Journal of Fuzzy System 7(3), 109–118 (2005)Google Scholar
  10. 10.
    Hamilton, J.D.: Time Series Analysis. Princeton University Press, New Jersey (1994)MATHGoogle Scholar
  11. 11.
    Hentschel, L.: All in the Family: Nesting Symmetric and Asymmetric GARCH Models. Journal of Financial Economics 39, 71–104 (1995)CrossRefGoogle Scholar
  12. 12.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)MATHGoogle Scholar
  13. 13.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines (and other kernel-based learning methods). Cambridge University Press, London (2000)Google Scholar
  14. 14.
    Kreyszig, E.: Advanced Engineering Mathematics, 8th edn. Wiley, New York (1999)Google Scholar
  15. 15.
    Chang, B.R.: Forecasting the Flow of Data Packets in Web Using ANFISCH Predictor Tuned by Segmented Adaptive Support Vector Regression. In: Proc. The 5th International Conference on Computer and Information Technology, Fudan University, Shanghai, China, September 21-23, 2005, pp. 23–27 (2005)Google Scholar
  16. 16.
    Chang, B.R.: Applying Nonlinear Generalized Autoregressive Conditional Heteroscedasticity to Compensate ANFIS Outputs Tuned by Adaptive Support Vector Regression. Fuzzy Sets and Systems 157(13), 1832–1850 (2004)Google Scholar
  17. 17.
    Inflow and Outflow of data packets by bits per second in WWW server, computer center, NTTU,
  18. 18.
    Inflow and Outflow of data packets by bits per second in WWW server, computer center, STU,
  19. 19.
    Ljung, G.M., Box, G.E.P.: On a Measure of Lack of Fit in Time Series Models. Biometrika 65, 67–72 (1978)CrossRefGoogle Scholar
  20. 20.
    Diebold, F.X.: Elements of Forecasting. South-Western, Cincinnati (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bao Rong Chang
    • 1
  • Shi-Huang Chen
    • 2
  • Hsiu Fen Tsai
    • 3
  1. 1.Department of Computer Science and Information EngineeringNational Taitung UniversityTaiwan
  2. 2.Department of Computer Science and Information EngineeringShu-Te UniversityKaohsiungTaiwan
  3. 3.Department of International BusinessShu-Te UniversityKaohsiungTaiwan

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