Multi-scale high-speed network traffic prediction using k-factor Gegenbauer ARMA model

Gegenbauer autoregressive moving average (GARMA) model has the ability to capture both the short- and long-range dependent characteristics of the underlying data. GARMA has been used for modeling and forecasting of the financial time series that exhibits a long-range dependency (LRD). Since the high-speed network traffic exhibits a high degree of LRD characteristic, GARMA could be used for its modeling and prediction. In this paper, we present a simplified parameter estimation procedure and an adaptive prediction scheme for the k-factor GARMA model. The adaptation gives the model the ability to capture, the non-stationary characteristic of the data. The k-factor GARMA is applied to model four different types of real traffic data: MPEG and JPEG video, Ethernet and Internet. These models are then used to predict one-step-ahead traffic value at different timescales. The results show that the estimated parameters of the k-factor GARMA model provide a detailed and accurate presentation for the traffic characteristics in both time and frequency domain. We also demonstrate that the prediction performance of the k-factor GARMA model outperforms that of the traditional autoregressive (AR) model.