Time series synchronization in cross-recurrence networks: uncovering a homomorphic law across diverse complex systems
Exploring the synchronicity between time series, especially the similar patterns during extreme events, has been a focal point of research in academia. This is due to the fact that such special dependence occurring between pairs of time series often plays a crucial role in triggering emergent behavi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ad1dc5 |
| _version_ | 1797347683611967488 |
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| author | Shijia Song Handong Li |
| author_facet | Shijia Song Handong Li |
| author_sort | Shijia Song |
| collection | DOAJ |
| description | Exploring the synchronicity between time series, especially the similar patterns during extreme events, has been a focal point of research in academia. This is due to the fact that such special dependence occurring between pairs of time series often plays a crucial role in triggering emergent behaviors in the underlying systems and is closely related to systemic risks. In this paper, we investigate the relationship between the synchronicity of time series and the corresponding topological properties of the cross-recurrence network (CRN). We discover a positive linear relationship between the probability of pairwise time series event synchronicity and the corresponding CRN’s clustering coefficient. We first provide theoretical proof, then demonstrate this relationship through simulation experiments by coupled map lattices. Finally, we empirically analyze three instances from financial systems, Earth’s ecological systems, and human interactive behavioral systems to validate that this regularity is a homomorphic law in different complex systems. The discovered regularity holds significant potential for applications in monitoring financial system risks, extreme weather events, and more. |
| first_indexed | 2024-03-08T11:52:18Z |
| format | Article |
| id | doaj.art-5e4bd23a24fd4fbda3047e6174fb75ca |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-08T11:52:18Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-5e4bd23a24fd4fbda3047e6174fb75ca2024-01-24T06:51:06ZengIOP PublishingNew Journal of Physics1367-26302024-01-0126101304410.1088/1367-2630/ad1dc5Time series synchronization in cross-recurrence networks: uncovering a homomorphic law across diverse complex systemsShijia Song0Handong Li1https://orcid.org/0000-0003-3613-7327School of Systems Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaSchool of Systems Science, Beijing Normal University , Beijing 100875, People’s Republic of ChinaExploring the synchronicity between time series, especially the similar patterns during extreme events, has been a focal point of research in academia. This is due to the fact that such special dependence occurring between pairs of time series often plays a crucial role in triggering emergent behaviors in the underlying systems and is closely related to systemic risks. In this paper, we investigate the relationship between the synchronicity of time series and the corresponding topological properties of the cross-recurrence network (CRN). We discover a positive linear relationship between the probability of pairwise time series event synchronicity and the corresponding CRN’s clustering coefficient. We first provide theoretical proof, then demonstrate this relationship through simulation experiments by coupled map lattices. Finally, we empirically analyze three instances from financial systems, Earth’s ecological systems, and human interactive behavioral systems to validate that this regularity is a homomorphic law in different complex systems. The discovered regularity holds significant potential for applications in monitoring financial system risks, extreme weather events, and more.https://doi.org/10.1088/1367-2630/ad1dc5time series analysiscross-recurrence networkevent synchronicitycomplex systems |
| spellingShingle | Shijia Song Handong Li Time series synchronization in cross-recurrence networks: uncovering a homomorphic law across diverse complex systems New Journal of Physics time series analysis cross-recurrence network event synchronicity complex systems |
| title | Time series synchronization in cross-recurrence networks: uncovering a homomorphic law across diverse complex systems |
| title_full | Time series synchronization in cross-recurrence networks: uncovering a homomorphic law across diverse complex systems |
| title_fullStr | Time series synchronization in cross-recurrence networks: uncovering a homomorphic law across diverse complex systems |
| title_full_unstemmed | Time series synchronization in cross-recurrence networks: uncovering a homomorphic law across diverse complex systems |
| title_short | Time series synchronization in cross-recurrence networks: uncovering a homomorphic law across diverse complex systems |
| title_sort | time series synchronization in cross recurrence networks uncovering a homomorphic law across diverse complex systems |
| topic | time series analysis cross-recurrence network event synchronicity complex systems |
| url | https://doi.org/10.1088/1367-2630/ad1dc5 |
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