This article considers changepoint detection in serially correlated categorical time series. While changepoint aspects in correlated sequences of continuous random variables have been extensively explored in the literature, changepoint methods for independent categorical time series are only now coming into vogue. This study extends changepoint methods by developing techniques for correlated categorical time series. Here, a cumulative sum type test is devised to test for a single changepoint in a correlated categorical data sequence. Our categorical series is constructed from a latent Gaussian process through clipping techniques. A sequential parameter estimation method is proposed to estimate the parameters in this model. The methods are illustrated via simulations and applied to a real categorized rainfall time series from Albuquerque, New Mexico.

中文翻译：

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自相关序数分类时间序列中的变化点检测

本文考虑序列相关的分类时间序列中的变化点检测。虽然文献中已经广泛探讨了连续随机变量相关序列中的变化点方面，但独立分类时间序列的变化点方法现在才开始流行。本研究通过开发相关分类时间序列的技术来扩展变点方法。在这里，设计了一个累积和类型测试来测试相关分类数据序列中的单个变化点。我们的分类系列是通过裁剪技术从潜在高斯过程构建的。提出了一种序列参数估计方法来估计该模型中的参数。这些方法通过模拟进行说明，并应用于阿尔伯克基的真实分类降雨时间序列，