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- W2952202224 abstract "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." @default.
- W2952202224 created "2019-06-27" @default.
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- W2952202224 date "2014-05-15" @default.
- W2952202224 modified "2023-09-25" @default.
- W2952202224 title "Effective Bayesian Modeling of Groups of Related Count Time Series" @default.
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