Complex network approach to characterize the statistical features of the sunspot series
dc.bibliographicCitation.firstPage | 13051 | eng |
dc.bibliographicCitation.lastPage | 1932 | eng |
dc.bibliographicCitation.volume | 16 | eng |
dc.contributor.author | Zou, Y. | |
dc.contributor.author | Small, M. | |
dc.contributor.author | Liu, Z. | |
dc.contributor.author | Kurths, J. | |
dc.date.accessioned | 2020-08-01T15:36:08Z | |
dc.date.available | 2020-08-01T15:36:08Z | |
dc.date.issued | 2014 | |
dc.description.abstract | Complex network approaches have been recently developed as an alternative framework to study the statistical features of time-series data. We perform a visibility-graph analysis on both the daily and monthly sunspot series. Based on the data, we propose two ways to construct the network: one is from the original observable measurements and the other is from a negative-inverse- transformed series. The degree distribution of the derived networks for the strong maxima has clear non-Gaussian properties, while the degree distribution for minima is bimodal. The long-term variation of the cycles is reflected by hubs in the network that span relatively large time intervals. Based on standard network structural measures, we propose to characterize the long-term correlations by waiting times between two subsequent events. The persistence range of the solar cycles has been identified over 15-1000 days by a power-law regime with scaling exponent γ = 2.04 of the occurrence time of two subsequent strong minima. In contrast, a persistent trend is not present in the maximal numbers, although maxima do have significant deviations from an exponential form. Our results suggest some new insights for evaluating existing models. | eng |
dc.description.version | publishedVersion | eng |
dc.identifier.uri | https://doi.org/10.34657/3890 | |
dc.identifier.uri | https://oa.tib.eu/renate/handle/123456789/5261 | |
dc.language.iso | eng | eng |
dc.publisher | Bristol : Institute of Physics Publishing | eng |
dc.relation.doi | https://doi.org/10.1088/1367-2630/16/1/013051 | |
dc.relation.ispartofseries | New Journal of Physics 16 (2014) | eng |
dc.relation.issn | 1367-2630 | |
dc.rights.license | CC BY 3.0 Unported | eng |
dc.rights.uri | https://creativecommons.org/licenses/by/3.0/ | eng |
dc.subject | complex network | eng |
dc.subject | time-series data | eng |
dc.subject | sunspots | eng |
dc.subject.ddc | 530 | eng |
dc.title | Complex network approach to characterize the statistical features of the sunspot series | eng |
dc.type | article | eng |
dc.type | Text | eng |
dcterms.bibliographicCitation.journalTitle | New Journal of Physics | eng |
tib.accessRights | openAccess | eng |
wgl.contributor | PIK | eng |
wgl.subject | Physik | eng |
wgl.type | Zeitschriftenartikel | eng |
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