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A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction

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DOI: 10.4236/ijcns.2015.84005    3,294 Downloads   4,056 Views   Citations

ABSTRACT

This paper highlights the statistical procedure used in developing models that have the ability of capturing and forecasting the traffic of mobile communication network operating in Vietnam. To build such models, we follow Box-Jenkins method to construct a multiplicative seasonal ARIMA model to represent the mean component using the past values of traffic, then incorporate a GARCH model to represent its volatility. The traffic is collected from EVN Telecom mobile communication network. Diagnostic tests and examination of forecast accuracy measures indicate that the multiplicative seasonal ARIMA/GARCH model, i.e. ARIMA (1, 0, 1) × (0, 1, 1)24/GARCH (1, 1) shows a good estimation when dealing with volatility clustering in the data series. This model can be considered to be a flexible model to capture well the characteristics of EVN traffic series and give reasonable forecasting results. Moreover, in such situations that the volatility is not necessary to be taken into account, i.e. short-term prediction, the multiplicative seasonal ARIMA/GARCH model still acts well with the GARCH parameters adjusted to GARCH (0, 0).

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Tran, Q. , Ma, Z. , Li, H. , Hao, L. and Trinh, Q. (2015) A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction. International Journal of Communications, Network and System Sciences, 8, 43-49. doi: 10.4236/ijcns.2015.84005.

References

[1] Shu, Y.T., Yu, M.F., Liu, J.K. and Yang, O.W.W. (2003) Wireless Traffic Modeling and Prediction Using Seasonal ARIMA Models. IEEE.
[2] Yu, Y.H., Wang, J., Song, M.N. and Song, J.D. (2010) Network Traffic Prediction and Result Analysis Based on Seasonal ARIMA and Correlation Coefficient. 2010 International Conference on Intelligent System Design and Engineering Application. http://dx.doi.org/10.1109/ISDEA.2010.335
[3] Guo, J., Peng, Y., Peng, X.Y., Chen, Q., Yu, J. and Dai, Y.F. (2009) Traffic Forecasting for Mobile Networks with Multiplicative Seasonal ARIMA Models. The Ninth In-ternational Conference on Electronic Measurement & Instruments.
[4] Chen, H., Wan, Q.L., Zhang, B., Li, F.X. and Wang, Y.R. (2010) Short-Term Load Forecasting Based on Asymmetric ARCH Models. IEEE.
[5] Chen, C.Y., Hu, J.M., Meng, Q. and Zhang, Y. (2011) Short-Time Traffic Flow Prediction with ARIMA-GARCH Model. IEEE Intelligent Vehicles Sym-posium (IV), Baden-Baden, 5-9 June.
[6] Zhou, B., He, D. and Sun, Z.L. (2006) Traffic Predictability Based on ARIMA/GARCH Model. 2nd Conference on Next Generation Internet Design and Engineering.
[7] Radha, S. and Then-mozhi, M. (2006) Forecasting Short Term Interest Rates Using ARMA, ARMA-GARCH and ARMA-EGARCH Mod-els.
[8] Nian, L.C. (2009) Application of ARIMA and GARCH Models in Forecasting Crude Oil Prices. A Dissertation Submitted in Partial Fulfillment of the Requirement for the Award of the Degree of Master of Science (Mathemat-ics).
[9] Ramon, H.L. (2008) Forecasting the Volatility of Philippine Inflation Using GARCH Models. BSP Working Paper Series, Series No. 2008-01.
[10] Kim, S. (2011) Forecasting Internet Traffic by Using Seasonal GARCH Models. Journal of Communications and Networks, 13. http://dx.doi.org/10.1109/JCN.2011.6157478
[11] Dong, N.Q. (2008) Econometrics—Advanced Program. Science and Technique Publisher.
[12] Dong, N.Q. (2008) Econometrics Assignments—With EVIEWS. Science and Technique Publisher.
[13] Quynh, N.H. (2004) Time Series Analysis and Identification. Science and Technique Publisher.
[14] Agung, I.G.N. (2009) Time Series Data Analysis Using EViews. John Wiley & Sons (Asia) Pte Ltd.
[15] (2007) EViews 6 User’s Guide I. Quantitative Micro Software, LLC.
[16] (2007) EViews 6 User’s Guide II. Quantitative Micro Software, LLC.

  
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