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 …

The R package tscount provides likelihood-based estimation methods for analysis and
modeling of count time series following generalized linear models. This is a flexible class of …

The sports domain presents a number of significant computational challenges for artificial
intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that …

We present a statistical model which can be employed to understand the contagion
dynamics of the COVID-19, which can heavily impact health, economics and finance. The …

We investigate the state-of-the-art in score-based soccer match outcome modelling to
identify the top-performing methods across diverse classes of existing approaches to the …

The availability of intensive care beds during the COVID‐19 epidemic is crucial to guarantee
the best possible treatment to severely affected patients. In this work we show a simple …

We present a statistical model that can be employed to monitor the time evolution of the
COVID‐19 contagion curve and the associated reproduction rate. The model is a Poisson …

Abstract Markov Chain Monte Carlo (MCMC) methods have been shown to be a useful tool
in many branches in statistics. However, due to the complex structure of the models, this …

Although there are many contributions in the time series clustering literature, few studies still
deal with count time series data. This paper aims to develop a fuzzy clustering procedure for …

We propose an innovative approach to model and predict the outcome of football matches
based on the Poisson autoregression with exogenous covariates (PARX) model recently …