# Multivariate Count Time Series Modelling

arXiv: https://arxiv.org/abs/2103.08028 · HTML: https://mlsanity.com/paper/2103.08028/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments](https://mlsanity.com/paper/2501.14698/) (2025)
- [Poisson Autoregression on a Large Network with a Stochastic Block Model Structure](https://mlsanity.com/paper/2610.05365/) (2026)

## Abstract (arXiv)

We review autoregressive models for the analysis of multivariate count time series. In doing so, we discuss the choice of a suitable distribution for a vectors of count random variables. This review focus on three main approaches taken for multivariate count time series analysis: (a) integer autoregressive processes, (b) parameter-driven models and (c) observation-driven models. The aim of this work is to highlight some recent methodological developments and propose some potentially useful research topics.