# Clustering Discrete-Valued Time Series

## TL;DR

This paper develops a clustering approach for discrete-valued time series using INAR models within a finite mixture framework, enabling effective clustering and model selection.

## Contribution

It introduces a novel combination of INAR models with finite mixture models for clustering discrete-valued time series data.

## Key findings

- Effective clustering demonstrated on real data
- Model selection and estimation via EM algorithm
- Applicable to various discrete time series datasets

## Abstract

There is a need for the development of models that are able to account for discreteness in data, along with its time series properties and correlation. Our focus falls on INteger-valued AutoRegressive (INAR) type models. The INAR type models can be used in conjunction with existing model-based clustering techniques to cluster discrete-valued time series data. With the use of a finite mixture model, several existing techniques such as the selection of the number of clusters, estimation using expectation-maximization and model selection are applicable. The proposed model is then demonstrated on real data to illustrate its clustering applications.