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.

中文翻译：

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聚类离散值时间序列

需要开发能够解决数据离散性，时间序列属性和相关性的模型。我们的重点放在整数值自回归（INAR）类型的模型上。INAR类型模型可与现有的基于模型的聚类技术结合使用，以对离散值时间序列数据进行聚类。通过使用有限混合模型，可以应用几种现有技术，例如选择聚类数量，使用期望最大化进行估计和模型选择。然后在真实数据上演示提出的模型，以说明其聚类应用。