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| Titel |
Multiplex Recurrence Networks |
| VerfasserIn |
Deniz Eroglu, Norbert Marwan |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250139587
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| Publikation (Nr.) |
 EGU/EGU2017-2854.pdf |
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| Zusammenfassung |
| The complex nature of a variety of phenomena in physical, biological, or earth
sciences is driven by a large number of degrees of freedom which are strongly
interconnected. Although the evolution of such systems is described by multivariate time
series (MTS), so far research mostly focuses on analyzing these components one by
one.
Recurrence based analyses are powerful methods to understand the underlying dynamics
of a dynamical system and have been used for many successful applications including
examples from earth science, economics, or chemical reactions. The backbone of these
techniques is creating the phase space of the system. However, increasing the dimension of a
system requires increasing the length of the time series in order get significant and
reliable results. This requirement is one of the challenges in many disciplines, in
particular in palaeoclimate, thus, it is not easy to create a phase space from measured
MTS due to the limited number of available obervations (samples). To overcome
this problem, we suggest to create recurrence networks from each component of
the system and combine them into a multiplex network structure, the multiplex
recurrence network (MRN). We test the MRN by using prototypical mathematical models
and demonstrate its use by studying high-dimensional palaeoclimate dynamics
derived from pollen data from the Bear Lake (Utah, US). By using the MRN, we can
distinguish typical climate transition events, e.g., such between Marine Isotope Stages. |
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