Traffic Forecasting and Planning of WiMAX under Multiple Priority Using Fuzzy Time Series Analysis


Network traffic prediction plays a fundamental role in characterizing the network performance and it is of significant interests in many network applications, such as admission control or network management. Therefore, The main idea behind this work, is the development of a WIMAX Traffic Forecasting System for predicting traffic time series based on the daily and monthly traffic data recorded (TRD) with association of feed forward multi-layer perceptron (FFMLP). The quality of forecasting WIMAX Traffic obtained by comparing different configurations of the FFMLP that consist of investigating different FFMLP model architectures and different Learning Algorithms. The decision of changing the FFMLP architecture is essentially based on prediction results to obtain the FFMLP model for flow traffic prediction model. The different configurations were tested using daily and monthly real traffic data recorded at each of the two base stations (A and B) that belongs to a Libyan WiMAX Network. We evaluate our approach with statistical measurement and a good statistic measure of FMLP indicates the performance of selected neural network configuration. The developed system indicates promising results in which it successfully network traffic prediction through daily and monthly traffic data recorded (TRD) association with artificial neural network.

Share and Cite:

Abdullah, I. , Daw, D. and Seman, K. (2015) Traffic Forecasting and Planning of WiMAX under Multiple Priority Using Fuzzy Time Series Analysis. Journal of Applied Mathematics and Physics, 3, 68-74. doi: 10.4236/jamp.2015.31009.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Wang, L.X. (1997) A Course in Fuzzy Systems and Control. Prentice-Hall, Inc., New Jersey.
[2] Zhang, G., Patuwo, B.E. and Hu, M.Y. (1998) Forecasting with Artificial Neural Networks: The State of the Art. International Journal of Forecasting, 14, 35-62.
[3] Zhang, B.-L., Coggins, R., Jabri, M.A., Dersch, D. and Flower, B. (2001) Multiresolution Forecasting for Future Trading Using Wavelet Decomposition. IEEE Transactions on Neural Networks, 12, 765-775.
[4] Zhang, G.P. and Qi, M. (2005) Neural Network Forecasting for Seasonal and Trend Time Series. European Journal of Operation Research, 160, 501-514.
[5] Popoola, A.O. (2007) Fuzzy-Wavelet Method for Time Series Analysis, Dissertation. Department of Computing, School of Electronics and Physical Sciences, University of Surrey, Guildford, UK.
[6] Tseng, F.-M., Tseng, G.-H., Yu, H.-C. and Yuan, B.J.C. (2001) Fuzzy ARIMA Model for Fore-casting The Foreign Exchange Market. Fuzzy Sets and Systems, 118, 9-19.
[7] Song, Q. and Chissom, B.S. (1993) Forecasting Enrollments with Fuzzy Time series Part I. Fuzzy Sets and Systems, 54, 1-9.
[8] Mitra, A. and Mitra, S. (2006) Modeling Exchange Rates Using Wavelet Decomposed Genetic Neural Networks. Statistical Methodology, 3, 103-124.
[9] Daw, D.A.A. and Seman, K.B. (2013) Gateway to Wimax Profiling Services in Libya. International Journal of Engineering Research and Development, 7, 63-68.
[10] Ion Railean, D., Stolojescu, C., Moga, S. and Lenca, P. (2010) WIMAX Traffic Forecasting based on Neural Networks in Wavelet Domain. 2010 Fourth International Conference on Ion Research Challenges in Information Science (RCIS), 443-452.
[11] Firoiu, I. and Stolojescu, C. and Isar, A. (2009) Forecasting of WiMAX BS Traffic: Observations and Initial Models, Alcatel Lucent Technical Report, January 2009.
[12] Zhang, G.P. (2003) Time Series Forecasting Using a Hybrid ARIMA and Neural Network Model. Neurocomputing, 50, 159-175.
[13] Liu, H.T. (2009) An Integrated Fuzzy Time Series Forecasting System. Expert Systems with Applications, 36, 10045- 10053.
[14] Yang, Q. and Xindong, W. (2006) 10 Challenging Problems in Data Mining Research. International Journal of Information Technology and Decision Making, 5, 597-604.
[15] Ibarra-Berastegi, G., Elias, A., Arias, R. and Barona, A. (2007) Artificial Neural Networks vs Linear Regression in a Fluid Mechanics and Chemical Modelling Problem: Elimination of Hydrogen Sulphide in a Lab-Scale Biofilter. IEEE/ACS International Conference on Computer Systems and Applications, 584-587.
[16] Chen, S.M. (1996) Forecasting Enrollments Based on Fuzzy Time Series. Fuzzy Sets and Systems, 81, 311-319.
[17] Yu, H.K. (2005) Weighted Fuzzy Time-Series Models for TAIEX Forecasting. Physica A: Statistical Mechanics and its Applications, 349, 609-624.
[18] Cheng, C.H., Chen, T.L., Teoh, H.J. and Chiang, C.H. (2008) Fuzzy Time Series Based on Adaptive Expectation Model for TAIEX Forecasting. Expert Systems with Applications, 34, 1126-1132.
[19] Lee, M.H. and Suhartono (2010) A Novel Weighted Fuzzy Time Series Model for Forecasting Seasonal Data. Proceeding 2nd International Conference on Mathematical Sciences, Kuala Lumpur, Malaysia, 332-340.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.