Clustering discrete-valued time series
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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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