This article aims at providing a comprehensive survey of recent developments in the field of
integer-valued time series modelling, paying particular attention to models obtained as …

In this article, we present an overview of recent advances in Bayesian modeling and
analysis of multivariate time series of counts. We discuss basic modeling strategies …

Linear processes have been one of the most fundamental tools in modeling serially
dependent data. These models and methods heavily depend on Gaussian processes and …

In this work we consider the problem of forecasting integer-valued time series, modelled by
the INAR (1) process introduced by McKenzie (1985) and Al-Osh and Alzaid (1987). The …

Discrete-valued time series are common in practice, yet methods for their analysis have
been developed only recently. The fact that the variables take values on a finite or countably …

The analysis of solar irradiance has important applications in predicting solar energy
production from solar power plants. Although the sun provides every day more energy than …

In many phenomena described by stochastic processes, the implementation of an alarm
system becomes fundamental to predict the occurrence of future events. In this work we …

This study explores zero-inflated count time series models used to analyze data sets with
characteristics such as overdispersion, excess zeros, and autocorrelation. Specifically, we …

In this article the contributions of weather on the occurrences of road accidents in Tanzania
particularly for Morogoro and Pwani Regions is discussed. The correlation between road …

Outlying observations are commonly encountered in the analysis of time series. In this paper
a Bayesian approach is employed to detect additive outliers in order one Poisson integer …