论文标题
基于聚类的多变量时间序列的双重演变：分析Covid-19
Cluster-based dual evolution for multivariate time series: analyzing COVID-19
论文作者
论文摘要
本文提出了一种基于群集的方法来分析多元时间序列的演变，并将其应用于COVID-19大流行。每天，我们根据其案件和死亡计数分为集群。集群和各个国家的集群成员资格的总数是算法确定的。我们研究了随着时间的流逝，这两个量的变化都表明案件和死亡的演变有着紧密的相似性。案件群数的变化数量是死亡的数量计数为32天。另一方面，相对于群集分组之间的最大一致性，最佳偏移是通过比较亲和力矩阵的新方法确定的。考虑到这一偏见，我们确定了从199例19例死亡的进展中的异常国家。该分析可以有助于强调最低限制一个国家的Covid-19死亡率，以强调最不重要的公共政策。
This paper proposes a cluster-based method to analyze the evolution of multivariate time series and applies this to the COVID-19 pandemic. On each day, we partition countries into clusters according to both their case and death counts. The total number of clusters and individual countries' cluster memberships are algorithmically determined. We study the change in both quantities over time, demonstrating a close similarity in the evolution of cases and deaths. The changing number of clusters of the case counts precedes that of the death counts by 32 days. On the other hand, there is an optimal offset of 16 days with respect to the greatest consistency between cluster groupings, determined by a new method of comparing affinity matrices. With this offset in mind, we identify anomalous countries in the progression from COVID-19 cases to deaths. This analysis can aid in highlighting the most and least significant public policies in minimizing a country's COVID-19 mortality rate.
