A growing interest in non-Gaussian time series, particularly in series comprised of
nonnegative integers (counts), is taking place in today's statistics literature. Count series …

A much-needed introduction to the field of discrete-valued time series, with a focus on count-
data time series Time series analysis is an essential tool in a wide array of fields, including …

This article discusses the modelling of integer‐valued time series with overdispersion and
potential extreme observations. For the problem, a negative binomial INGARCH model, a …

This paper is concerned with a general class of observation‐driven models for time series of
counts whose conditional distributions given past observations and explanatory variables …

This paper introduces and evaluates new models for time series count data. The
Autoregressive Conditional Poisson model (ACP) makes it possible to deal with issues of …

This paper studies theory and inference related to a class of time series models that
incorporates nonlinear dynamics. It is assumed that the observations follow a one-parameter …

Various models for time series of counts which can account for discreteness, overdispersion
and serial correlation are compared. Besides observation-and parameter-driven models …

We present a method for estimating event-count models when the data is generated from a
persistent time-series process. A Kalman filter is used to estimate a Poisson exponentially …

Organizations with large-scale inventory systems typically have a large proportion of items
for which demand is intermittent and low volume. We examine various different approaches …

We review regression models for count time series. We discuss the approach that is based
on generalized linear models and the class of integer autoregressive processes. The …