  ICT systems provide detailed information on computer network traffic.
However, due to storage limitations, some of the information on past traffic is
often only retained in an aggregated form. In this paper we show that
  Linear Gaussian State Space Models yield simple yet effective methods to make
predictions based on time series at different aggregation levels. The models
link coarse-grained and fine-grained time series to a single model that is able
to provide fine-grained predictions. Our numerical experiments show up to 3.7
times improvement in expected mean absolute forecast error when forecasts are
made using, instead of ignoring, additional coarse-grained observations. The
forecasts are obtained in a Bayesian formulation of the model, which allows for
provisioning of a traffic prediction service with highly informative priors
obtained from coarse-grained historical data.
