Clustering Discrete-Valued Time Series
Methodology
2020-03-31 v2 Applications
Machine Learning
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.
Cite
@article{arxiv.1901.09249,
title = {Clustering Discrete-Valued Time Series},
author = {Tyler Roick and Dimitris Karlis and Paul D. McNicholas},
journal= {arXiv preprint arXiv:1901.09249},
year = {2020}
}