# Clustering Discrete-Valued Time Series

arXiv: https://arxiv.org/abs/1901.09249 · HTML: https://mlsanity.com/paper/1901.09249/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2019.

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

- [Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models](https://mlsanity.com/paper/2208.11907/) (2022)
- [Variational Markov chain mixtures with automatic component selection](https://mlsanity.com/paper/2406.04653/) (2024)

## Abstract (arXiv)

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.