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.
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Acknowledgements
The authors are grateful to anonymous reviewers for their very helpful comments. This work was supported by the Canada Research Chairs program and an E.W.R. Steacie Memorial Fellowship (McNicholas).
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Roick, T., Karlis, D. & McNicholas, P.D. Clustering discrete-valued time series. Adv Data Anal Classif 15, 209–229 (2021). https://doi.org/10.1007/s11634-020-00395-7
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DOI: https://doi.org/10.1007/s11634-020-00395-7