具有共同结构变化的时间依赖观测的基于模型的聚类
Model-based clustering of time-dependent observations with common structural changes
AI总结：
提出一种基于模型的时间序列聚类方法，通过随机阶数的潜在表示将结构变化时间相同的观测归为一类，可结合多种时间依赖模型，并应用于欧盟国家COVID-19传播的结构变化聚类。
摘要：
我们提出了一种针对时间序列样本的基于模型的聚类新方法。我们假设一种独特的共同性：如果两次观测的行为结构变化发生在同一时间，则它们属于同一组。我们利用基于随机阶数的每条时间序列结构变化的潜在表示，来诱导不同观测之间的联系。这种方法形成了一种通用的建模策略，可以与文献中已知的许多时间依赖模型相结合。我们的研究受到一个流行病学问题的启发，我们希望为欧盟不同国家提供聚类，如果COVID-19病毒传播过程的结构变化发生在同一时间，则两个国家属于同一聚类。
英文摘要：
We propose a novel model-based clustering approach for samples of time series. We assume as a unique commonality that two observations belong to the same group if structural changes in their behaviours happen at the same time. We resort to a latent representation of structural changes in each time series based on random orders to induce ties among different observations. Such an approach results in a general modeling strategy and can be combined with many time-dependent models known in the literature. Our studies have been motivated by an epidemiological problem, where we want to provide clusters of different countries of the European Union, where two countries belong to the same cluster if the spreading processes of the COVID-19 virus had structural changes at the same time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
