Multivariate Time-Series Prediction for Traffic in Large WAN Topology

Network traffic behavior is noisy and random, making it difficult to find patterns and predict future behavior. In this paper, we develop statistical models that use multivariate data model, incorporating seasonality, peak frequencies, and link relationships to improve future predictions. Using Fourier Transforms to extract seasons and peak frequencies from individual traces, we perform seasonality tests and ARIMA measures to determine optimal parameters to use in our prediction model. We develop a SARIMA multivariate model using real network traces to show improved prediction accuracy with better RMSE and smaller confidence intervals when compared to univariate approaches.