Count time series are widely available in fields such as epidemiology, finance, meteorology,
and sports, and thus there is a growing demand for both methodological and application …

To capture the multivariate count time series showing piecewise phenomena, we introduce
a class of first-order multivariate threshold integer-valued autoregressive process. The …

Autoregressive models are reviewed for the analysis of multivariate count time series. A
particular topic of interest which is discussed in detail is that of the choice of a suitable …

Many infectious diseases spread through person-to-person contact, either directly or
indirectly. The proposal incorporates spatial-temporal patterns into multivariate integer …

Knowledge of causal relationships is fundamental for understanding the dynamic
mechanisms of ecological systems. To detect such relationships from multivariate time …

In this paper, we propose a framework for modeling multivariate count time series data that
accommodates zero-inflated components. Our approach is based on a novel class of …

The novel circumstance-driven bivariate integer-valued autoregressive (CuBINAR) model
for non-stationary count time series is proposed. The non-stationarity of the bivariate count …

The univariate integer-valued time series has been extensively studied, but literature on
multivariate integer-valued time series models is quite limited and the complex correlation …

A new multivariate integer-valued Generalized AutoRegressive Conditional Heteroscedastic
(GARCH) process based on a multivariate Poisson generalized inverse Gaussian …

This paper is motivated by studies in neuroscience experiments to understand interactions
between nodes in a brain network using different types of data modalities that capture …