Bayesian hierarchical model with Dirichlet process learns latent heterogeneity in innovation rates of count time series and shows favorable forecasting on Pittsburgh crime data.
First-order integer-valued autoregressive (INAR(1)) pro- cess: distributional and regression properties
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Learning a latent pattern of heterogeneity in the innovation rates of a time series of counts
Bayesian hierarchical model with Dirichlet process learns latent heterogeneity in innovation rates of count time series and shows favorable forecasting on Pittsburgh crime data.