Clustering to Enhance Network Traffic Forecasting
Network traffic forecasting has become more and more vital and important in present days for monitoring the network traffic. The number of users that are connecting to network utilization is experiencing exponential growth. The accurate of modeling and forecasting network traffic is increasingly becoming significant in achieving guaranteed quality of service (QoS) in network. The enhanced QoS is maintained in the network by modeling and forecasting network. In this paper, we propose an integrated model that combines clustering with linear and nonlinear time series forecasting models, namely, Weighted Exponential Smoothing (WES) Holt Trend Exponential Smoothing (HTES) autoregressive moving average (ARMA) Hybrid model (Wavelet with WES) and AutoRegressive Neural Network (NARNET) models to enhance forecasting of loading packets in network. The experimental results show that the integrated model can be an effective way to enhance forecasting accuracy attained with assist of derived centriods. The performance measures MSE, RMSE, and MAPE are used to evaluate the results of conventional time series models and proposed model.