Effective Bayesian Modeling of Groups of Related Count Time Series
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(2):1395-1403, 2014.
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.