2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN 2016)

26 / 2016-07-15 15:46:29

Integration of Time Series Models with Soft Clustering to Enhance Network Traffic Forecasting

The network traffic forecasting is of significant interest in many domains such as bandwidth allocation, congestion control and network management. Hence, forecasting
of network traffic has received attention from the computer
networks field for achieving guaranteed Quality of Service
(QoS) in network.
In this paper, we propose a forecasting model that combines
conventional time series models with clustering approaches.
The conventional linear and non linear time series models
namely Weighted Exponential Smoothing (WES), Holt-Trend
Exponential Smoothing (HTES), AutoRegressive Moving Average (ARMA), Hybrid model (Wavelet with WES) and AutoRegrssive Neural Network (NARNET) models are applied for
forecasting network traffic. Our novelty is application of soft
clustering for enhancing the existing time series models that are
used to forecast network traffic. Clustering can model network
traffic data and its characteristics. We derived a methodology
to appropriately use cluster centriods to enhance the results
obtained by conventional approach. We experimented with
different soft clustering techniques such as Fuzzy C-Means
(FCM) and Rough K-Means (RKM) clustering to verify the
improvement in forecasting.
The results of our integrated model are validated using Mean
Square Error (MSE), Root Mean Square Error (RMSE) and
Mean Absolute Percentage Error (MAPE) performance measures. The results show that the integrated model enhances
the results obtained using conventional time series forecasting
models