  Time series of counts arise in a variety of forecasting applications, for
which traditional models are generally inappropriate. This paper introduces a
hierarchical Bayesian formulation applicable to count time series that can
easily account for explanatory variables and share statistical strength across
groups of related time series. We derive an efficient approximate inference
technique, and illustrate its performance on a number of datasets from supply
chain planning.
