Abstract
In this paper we discuss a class of models for time series of low count data based on the Generalized Linear Model (GLM) approach. Unlike the traditional Auto-Regressive Moving-Average (ARMA) models for continuous Gaussian data, these models capture both the temporal correlation structure and the discrete marginal distribution of count data. We focus on the properties, parameter estimation, and model adequacy aspects for count time series with Poisson or Negative Binomial conditional distributions. The properties and performance of these models are illustrated with synthetic and real data.
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