Identifying time series patterns is of great importance for many real-world problems in a variety of scientific fields. Here, we present a method to identify time series patterns in multiscale levels based on the hierarchical community representation in a complex network. The construction method transforms the time series into a network according to its segments’ correlation. The constructed network’s quality is evaluated in terms of the largest correlation threshold that reaches the largest main component’s size. The presence of repeated hierarchical patterns is then captured through network metrics, such as the modularity along the community detection process. We show the benefits of the proposed method by testing in one artificial dataset and two real-world time series applications. The results indicate that the method can successfully identify the original data’s hierarchical (micro and macro) characteristics.

识别时间序列模式对于各种科学领域的许多现实问题都非常重要。在这里，我们提出了一种基于复杂网络中的分层社区表示来识别多尺度级别的时间序列模式的方法。该构造方法根据其段的相关性将时间序列转换为网络。构建的网络的质量是根据达到最大主要组件大小的最大相关阈值来评估的。然后通过网络指标捕获重复分层模式的存在，例如社区检测过程中的模块化。我们通过在一个人工数据集和两个实际时间序列应用程序中进行测试来展示所提出方法的好处。