Knowing who follows whom and what patterns they are following are crucial steps to understand collective behaviors (e.g. a group of human, a school of fish, or a stock market). Time series is one of resources that can be used to get insight regarding following relations. However, the concept of following patterns or motifs and the solution to find them in time series are not obvious. In this work, we formalize a concept of following motifs between two time series and present a framework to infer following patterns between two time series. The framework utilizes one of efficient and scalable methods to retrieve motifs from time series called the Matrix Profile Method. We compare our proposed framework with several baselines. The framework performs better than baselines in the simulation datasets. In the dataset of sound recording, the framework is able to retrieve the following motifs within a pair of time series that two singers sing following each other. In the cryptocurrency dataset, the framework is capable of capturing the following motifs within a pair of time series from two digital currencies, which implies that the values of one currency follow the values of another currency patterns. Our framework can be utilized in any field of time series to get insight regarding following patterns between time series.

通过使用矩阵剖面方法，我们正式定义了一种在两个时间序列之间推断关注模式的理念，并提出了一个框架来推测两个时间序列之间的关注模式。该框架在模拟数据集中表现优于基线，并能够在音频记录和加密货币数据集中检测出关注模式。我们的框架可应用于任何时间序列领域，以获取关于时间序列之间关注模式的洞见。

基于矩阵剖析的时间序列变量滞后模式跟踪关系推理框架