Clustering discrete-valued time series

Tyler Roick, Dimitris Karlis and Paul D. McNicholas ()
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Tyler Roick: McMaster University
Dimitris Karlis: Athens University of Economics and Business
Paul D. McNicholas: McMaster University

Advances in Data Analysis and Classification, 2021, vol. 15, issue 1, No 10, 209-229

Abstract: 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.

Keywords: Finite mixture models; Model-based clustering; Discrete-valued time series; Autoregressive model; 62H30; 62M10 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11634-020-00395-7

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