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Clustering discrete-valued time series

Author

Listed:
  • Tyler Roick

    (McMaster University)

  • Dimitris Karlis

    (Athens University of Economics and Business)

  • Paul D. McNicholas

    (McMaster University)

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.

Suggested Citation

  • Tyler Roick & Dimitris Karlis & Paul D. McNicholas, 2021. "Clustering discrete-valued time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(1), pages 209-229, March.
    • Handle: RePEc:spr:advdac:v:15:y:2021:i:1:d:10.1007_s11634-020-00395-7
    DOI: 10.1007/s11634-020-00395-7
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    References listed on IDEAS

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