Class of Models for Aggregated Traffic Volume Time Series

Two time series are considered in this paper: one is the volume of hard disk activity, aggregated into half-hour periods, measured on a workstation, and the other is the volume of internet requests made to a workstation. Both of these time series exhibit features typical of network traffic data, namely, strong seasonal components and highly non-Gaussian distributions. For these time series, a particular class of nonlinear state-space models is proposed, and practical techniques for model-fitting and forecasting are demonstrated.