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We develop a Bayesian hierarchical semiparametric model for phenomena relatedto time series of counts. The main feature of the model is its capability tolearn a latent pattern of heterogeneity in the distribution of the processinnovation rates, which are softly clustered through time with the help of aDirichlet process placed at the top of the model hierarchy. The probabilisticforecasting capabilities of the model are put to test in the analysis of crimedata in Pittsburgh, with favorable results.

Learning a latent pattern of heterogeneity in the innovation rates of a time series of counts