A class of models for aggregated traffic volume time series

Summary. The development of time series models for traffic volume data constitutes an important step in constructing automated tools for the management of computing infrastructure resources. We analyse two traffic volume time series: one is the volume of hard disc 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 that are typical of network traffic data, namely strong seasonal components and highly non‐Gaussian distributions. For these time series, a particular class of non‐linear state space models is proposed, and practical techniques for model fitting and forecasting are demonstrated.