https://doi.org/10.1140/epjs/s11734-021-00163-4
Regular Article
Time series pattern identification by hierarchical community detection
1
ICMC, University of Sao Paulo, São Carlos, SP, Brazil
2
Institute of Computing, University of Campinas, Campinas, SP, Brazil
3
Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
4
FFCLRP, University of Sao Paulo, Ribeirão Preto, SP, Brazil
5
Universidade Católica de Brasília, Brasília, Brazil
Received:
29
November
2020
Accepted:
21
April
2021
Published online:
3
June
2021
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.
© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2021
