Clustering Discrete-Valued Time Series.

  title={Clustering Discrete-Valued Time Series.},
  author={Tyler Roick and Dimitris Karlis and Paul D. McNicholas},
  journal={arXiv: Methodology},
  • Tyler Roick, Dimitris Karlis, Paul D. McNicholas
  • Published 2020
  • Mathematics
  • arXiv: Methodology
  • 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… CONTINUE READING

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