Journal of Applied Mathematics and Physics, 2015, 3, **-** Published Online January 2015 in SciRes. http://www.scirp.org/journal/jamp doi How to cite this paper: Author 1, Author 2 and Author 3 (2015) Paper Title. Journal of Applied Mathematics and Physics, 3, **-**. http://dx.doi.org/10.4236/***.2015.***** Traffic Forecasting and Planning of WiMAX under Multiple Priority Using Fuzzy Time Series Analysis Ismail Bin Abdullah, Daw Abdulsalam Ali Daw, Kamaruzzaman Bin Seman Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM), Bandar Baru Nilai, Malaysia Email: isbah@usim.edu.my Received **** 2015 Abstract 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 net- work 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 ob- tain 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 configura- tion. 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. Keywords Network Traffic, Wimax, Fuzzy Time Series, Forecasting 1. Introduction Fuzzy systems are systems combining fuzzifier, fuzzy rule bases, fuzzy inference engine and defuzzifier (Wang, [1]). The systems have advantages that the developed models are characterized by linguistic interpretability and the generated rules can be understood, verified and extended. As a universal approximator, fuzzy systems have capability to model non stationary time series and give effect of data pre-processing on the forecast performance (Zhang, et al., [2] [3]; Zhang & Qi, [4]). Studying on data pre-processing using soft computing method has been done. Popoola [4] has analyzed effect of data pre-processing on the forecast performance of subtractive cluster-