Time series pattern identification by hierarchical community detection

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.