Artificial Neural Network Non-linear Auto Regressive Moving Average (NARMA) Model for Internet Traffic Prediction


  • Mohd Naqiuddin Sahrani Faculty of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, Malaysia
  • Md Mahfudz Mat Zan Faculty of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, Malaysia
  • Ihsan Mohd Yassin Faculty of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, Malaysia
  • Azlee Zabidi Faculty of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, Malaysia
  • Megat Syahirul Amin Megat Ali Faculty of Electrical Engineering, Universiti Teknologi Mara, Shah Alam, Malaysia


Network Traffic Prediction, Nonlinear AutoRegressive Moving Average (NARMA), System Identification, Forecasting,


The technology of computing and network communication is undergoing rapid development, leading to increasing number of applications and services being available online. As more applications are available online, network traffic becomes a significant problem as high network loads may limit access to users. In this paper, we propose an internet traffic Nonlinear Auto-Regressive Moving Average model (NARMA) prediction model to assist network managers in forecasting internet traffic and planning their resources accordingly. The Multi-Layer Perceptron (MLP) estimator was used in this paper. The performance of the model were evaluated using Mean Squared Error (MSE), correlation tests, and residual histogram tests with good agreement between the model and actual outputs.


S., Koyano, S., Ata ,I ., Oka, and K., Inoue, 2012. A High-Grained Traffic Prediction for Microseconds Power Control in Energy-Aware Routers. in Utility and Cloud Computing (UCC), 2012 IEEE Fifth International Conference on. 347-352.

B. Tao, G. Shanqing, Z. Zonghua, R. Ando, and Y. Kadobayashi. 2011. Practical network traffic analysis in P2P environment. in Wireless Communications and Mobile Computing Conference (IWCMC), 2011 7th International. 1801-1807.

M.J.O., Gavade and M.P.K., Kharat, 2011. Neural network based approach for mpeg video traffic prediction . 6

N., Zhenyu and G., Qing , 2011. An improved AQM scheme with adaptive reference queue threshold. in Communications and Networking in China (CHINACOM), 2011 6th International ICST Conference on. 589-593.

G., Terdik and T., Gyires, 2008. Internet Traffic Modeling with Levy Flights. in Networking, 2008. ICN 2008. Seventh International Conference on. 468-473.

K., Jian, Z., Dongwei, W., Xinyu ,W., Yanwen, and M., Qinghua, 2013. A network traffic prediction method using two-dimensional correlation and Single exponential smoothing. in Communication Technology (ICCT), 2013 15th IEEE International Conference on. 403-406.

R.C., Jaiswal and Lokhande S. D., 2013. Machine learning based internet traffic recognition with statistical approach. in India Conference (INDICON), 2013 Annual IEEE. 1-6.

Z.,Jun, C.,Chao, X.,Yang , Z.,Wanlei, and X.,Yong, 2013. Internet Traffic Classification by Aggregating Correlated Naive Bayes Predictions," Information Forensics and Security, IEEE Transactions on. 8:5-15.

M.H.F. Rahiman., 2008. System Identification of Essential Oil Extraction System. Ph. D. Dissertation. Faculty of Electrical

Engineering, Universiti Teknologi MARA. Shah Alam, Malaysia. [10] S., Su, W., Zhang, and S.,Zhao. 2014. Online fault prediction for nonlinear system based on sliding ARMA combined with online LSSVR. in Control Conference (CCC). 2014 33rd Chinese. 287-3291.

V.K., Dabhi and S., Chaudhary, 2014. Time Series Modeling and Prediction Using Postfix Genetic Programming," in Advanced

Computing & Communication Technologies (ACCT). 2014 Fourth International Conference on. 307-314.

Z., Run, C., Yinping, and F., Xing-an, 2012. A network traffic prediction model based on recurrent wavelet neural network. in Computer Science and Network Technology (ICCSNT). 2012 2nd International Conference on. 1630-1633.

I., Yassin , 2008. Face detection using artificial neural network trained on compact features and optimized using particle swarm optimization. M. S. Thesis, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam.

T., Liu. 2012. Optimizing mining association rules based on Artificial Neural Network," in World Automation Congress (WAC), 2012. 1-4.

F., Pilka and M., Oravec. 2011. Multi-step ahead prediction using neural networks," in ELMAR, 2011 Proceedings. 269-272.

A., RajaRajan, 2011. Brain disorder detection using artificial neural network," in Electronics Computer Technology (ICECT). 2011 3rd International Conference on. 268-272.

H., Chih-Chien, C., Wei-Chih , S., Chi-Yen, C., Yu-Ju, C., Chuo-Yean and H., Rey-Chue. 2010. Signal Processing by Polynomial NN and Equivalent Polynomial Function," in Pervasive Computing Signal Processing and Applications (PCSPA). 2010 First International Conference on. 460-463.

S, Haykin. 1999. Neural Networks: A Comprehensive Foundation. Delhi, India: Pearson Education (Singapore) Pte Ltd.

E.M.A.M., Mendes and S. A., Billings. 2001. An Alternative Solution to the Model Structure Selection Problem. IEEE Trans. Systems, Man & Cybernetics - Part A: Systems and Humans. 31(6):597-608.

X., Hong , R.J., Mitchell , S.., Chen, C.,.J., Harris, K., Li and G.,Irwin. W., 2008. Model selection approaches for non-linear system identification: a review. Int. J. of Systems Science. 39(10):925-946.

E. M. A. M , Mendes and S.A., Billings . 2001. An alternative solution to the model structure selection problem," IEEE Trans. Systems, Man and Cybernetics - Part A: Systems and Humans . 31(6):597-608.

N., Chiras, C., Evans and D., Rees . 2001. Nonlinear gas turbine modeling using NARMAX structures. IEEE Trans. Instrumentation and Measurement. 50(4): 893-898,

M.,Vallverdu, M.J., Korenberg and P., Caminal. 1991. Model

identification of the neural control of the cardiovascular system using NARMAX models. presented at the Proc. Computer in Cardiology.

S.L, Kukreja, H.,L., Galiana and R.E., Kearney, 2003. NARMAX representation and identification of ankle dynamics. IEEE Trans. Biomedical Eng. 50(1):70-81.

S. L., Kukreja, R. E., Kearney and H. L Galiana., 2005. A least squares parameter estimation algorithm for switched hammerstein systems with applications to the VOR. IEEE Trans. Biomedical Eng. 52(3):431-444.

N. A Rahim., M. N., Taib., and M. I., Yusof, 2003. Nonlinear system identification for a DC motor using NARMAX Approach. in Asian Conference on Sensors (AsiaSense). 305 - 311.

K. K Ahn. and H. P. H., Anh. 2010. Inverse Double NARX Fuzzy Modeling for System Identification. IEEE/ASME Trans. on

Mechatronics. 15(1):136-148.

Cheng Y., Wang L., Yu M., and Hu J., 2011. An efficient identification scheme for a nonlinear polynomial NARX model," Artificial Life Robotics. 16:70-73.

M., Shafiq and N.R., Butt . 2011. Utilizing Higher-Order Neural Networks in U-model Based Controllers for Stable Nonlinear Plants. Int. J. Control, Automation & Systems. 9(3):489-496.

Z. H. Chen and Y. Q. Ni, "On-board Identification and Control Performance Verification of an MR Damper Incorporated with Structure," J. Intelligent Material Systems and Structures, vol. 22, pp. 1551-1565, 2011.

B. H. G. Barbosa, L. A. Aguirre, C. B. Martinez, and A. P. Braga, "Black and Gray-Box Identification of a Hydraulic Pumping System," IEEE Trans. Control Systems Technology, vol. 19(2), pp. 398-406, 2011.

B. Cosenza, "Development of a neural network for glucose

concentration prevision in patients affected by type 1 diabetes," Bioprocess & Biosystems Engineering, pp. 1-9, 2012.

M. A., Balikhin , R.J.,Boynton, S. N., Walker, J. E., Borovsky , S.A.,Billings and H.L.,Wei. 2011. Using the NARMAX approach to model the evolution of energetic electrons fluxes at geostationary orbit," Geophysical Research Letters. 38(L18105):1-5.

H.L.,Wei and S.A.,Billings . 2008. An adaptive orthogonal search algorithm for model subset selection and non-linear system identification. Int. J. Control. 81(5):714-724.

H.L., Wei and S.A., Billings .2008. Model structure selection using an integrated forward orthogonal search algorithm assisted by squared correlation and mutual information. Int. J. Modeling, Identification & Control. 3(4):341-356.

L.A., Aguirre and C., Letellier . 2009. Modeling Nonlinear Dynamics and Chaos: A Review. Mathematical Problems in Engineering. 1-35.

L., Piroddi and M.,Lovera . 2008. NARX model identification with error filtering. in Proc. 17th World Congress Int. Federation of Automatic Control. Seoul, Korea. 2726-2731.

N. A. Jalil andI.Z.M., Darus . 2013. NARMA-L2 Vibration Controller for Flexible Structure with Non-Collocated Sensor-Actuator. presented at the Fifth International Conference on Computational Intelligence, Modelling and Simulation.

Z., Muhammad , Z.M.., Yusoff , M.H.F, Rahiman and M.N.,Taib. 2012. Modeling of steam distillation pot with ARX model. in Signal Processing and its Applications (CSPA). 2012 IEEE 8th International Colloquium on. 194-198.

S., Nomm, K., Vassiljeva, J., Belikov and E., Petlenkov. 2011. Structure identification of NN-ANARX model by genetic algorithm with combined cross-correlation-test based evaluation function. in Control and Automation (ICCA). 2011 9th IEEE International Conference on. 65-70.




How to Cite

Sahrani, M. N., Mat Zan, M. M., Mohd Yassin, I., Zabidi, A., & Megat Ali, M. S. A. (2017). Artificial Neural Network Non-linear Auto Regressive Moving Average (NARMA) Model for Internet Traffic Prediction. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-3), 145–149. Retrieved from