Predicting WAN traffic volumes using Fourier and multivariate SARIMA approach

Network traffic has been a critical issue that has attracted massive attention in network operations research and the industry. This paper tackles the need to understand traffic patterns across a high-speed network topology by developing statistical models that use multivariate data models, incorporating seasonality, peak frequencies, and link relationships to improve future predictions. Using Fourier transforms to extract seasons and peak frequencies, we perform seasonality tests and ARIMA measures to determine optimal parameters to use in our model. Our study shows that network traffic is non-stationary and possesses seasonality, making SARIMA the most suitable approach. Furthermore, based on network traces collected from multiple time zones of the R&E WAN, our results indicate an improved prediction accuracy of 93.7% from the multivariate model with better RMSE and smaller confidence intervals. Our work provides critical insights into studying network traffic and prediction methods necessary for future research in network capacity management.