Geometric detection of coupling directions by means of inter-system recurrence networks

Feldhoff, Jan H.; Donner, Reik V.; Donges, Jonathan F.; Marwan, Norbert; Kurths, Jürgen

doi:10.1016/j.physleta.2012.10.008

Cited by 96 publications

(94 citation statements)

References 62 publications

(123 reference statements)

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“…2 In the context of time series analysis, the interacting network representation has been applied for studying the structure of statistical interrelationships between different climatological fields with coupled climate networks 52,62 (Sec. III C) as well as for detecting the direction of coupling between complex dynamical systems using inter-system recurrence networks 63 (Sec. IV A).…”

Section: Measures and Models For Network Of Networkmentioning

confidence: 99%

“…123 Bivariate methods such as joint recurrence plots/networks, cross-recurrence plots, or inter-system recurrence networks can be used to investigate the coupling structure between two dynamical systems based on their time series, including methods to detect the directionality of coupling. 63,124,125 Recurrence analysis is applicable to general time series data from many fields such as climatology, medicine, neuroscience, or economics. 126 These applications range from using recurrence analysis as a classifier for monitoring health states in medicine and engineering 127 to detecting continental-scale nonlinear regime shifts in the Asian monsoon system during the Holocene.…”

Section: A Recurrence Analysismentioning

confidence: 99%

“…91,120 Specifically, it can be logarithmically transformed to yield the single-scale transitivity dimension 63 and complex synchronization scenarios including generalized synchronization. 133 The corresponding methodologies are represented by the classes timeseries.Inter SystemRecurrenceNetwork and timeseries.Joint RecurrenceNetwork, respectively.…”

Section: Recurrence Network Analysismentioning

confidence: 99%

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Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package

Donges

Heitzig

Beronov

et al. 2015

Chaos

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109

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We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni-and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.Network theory and nonlinear time series analysis provide powerful tools for the study of complex systems in various disciplines such as climatology, neuroscience, social science, infrastructure or economics. In the last years, combining both frameworks has yielded a wealth of new approaches for understanding and modeling the structure and dynamics of such systems based on the statistical analysis of network or uni-and multivariate time series. The pyunicorn software package (available at https://github.com/pik-copan/pyunicorn) facilitates the innovative synthesis of methods from network theory and nonlinear time series analysis in order to develop novel integrated methodologies. This paper provides an overview of the functionality provided by pyunicorn, introduces the theoretical concepts behind it and provides examples in the form of selected use cases mainly in the fields of climatology and paleoclimatology.

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Section: Measures and Models For Network Of Networkmentioning

confidence: 99%

Section: A Recurrence Analysismentioning

confidence: 99%

Section: Recurrence Network Analysismentioning

confidence: 99%

See 1 more Smart Citation

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package

Donges

Heitzig

Beronov

et al. 2015

Chaos

Self Cite

109

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“…We define the cross-degree k kl v , which gives the number of edges which connect vertex v in subgraph G k (i.e., v ∈ V k ) to any vertex in subgraph G l , as k kl v = q∈V l A vq [5,17]. The local cross-clustering coefficient C kl v estimates the probability that two randomly drawn neighbours of vertex v ∈ V k from subgraph G l are also neighbours [5]:…”

Section: Network Measures For Irnsmentioning

confidence: 99%

“…Available approaches range from using linear models [1], information based methods [2], synchronisation [3], to recurrence based approaches [4], to name a few. Here we propose a novel approach investigating the structures in a shared phase space of interacting dynamical systems by means of intersystem recurrence networks [5].…”

Section: Introductionmentioning

confidence: 99%

Detection of coupling directions with intersystem recurrence networks

Marwan

Feldhoff

Donner

et al. 2014

IEICE Proceeding Series

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Abstract-We describe and apply a novel concept for inferring coupling directions between dynamical systems based on geometric properties in phase space reconstructed from time series. The approach combines the recently introduced techniques for (1) studying interacting networks and (2) construction of complex networks from time series by their recurrence structure: we extend the approach of cross-recurrence between two systems towards an intersystem recurrence network and apply measures for studying interacting networks on it. These measures allow us to examine the emergence of typical geometric signatures in the driven relative to those of the driving system and vice versa, and, therefore, reveal signs of coupling directions. We demonstrate this concept by investigating the coupling between parts of the Asian monsoon system as seen from a palaeo-climate perspective.

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Interconnection between the Indian and the East Asian summer monsoon: Spatial synchronization patterns of extreme rainfall events

Gupta

Boers

et al. 2022

Intl Journal of Climatology

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A deeper understanding of the intricate relationship between the two components of the Asian summer monsoon (ASM)-the Indian summer monsoon (ISM) and the East Asian summer monsoon (EASM)-is crucial to improve the subseasonal forecasting of extreme precipitation events. Using an innovative complex network-based approach, we identify two dominant synchronization pathways between ISM and EASM-a southern mode between the Arabian Sea and southeastern China occurring in June, and a northern mode between the core ISM zone and northern China which peaks in July-and their associated large-scale atmospheric circulation patterns. Furthermore, we discover that certain phases of the Madden-Julian oscillation and the lower frequency mode of the boreal summer intraseasonal oscillation (BSISO) seem to favour the overall synchronization of extreme rainfall events between ISM and EASM while the higher-frequency mode of the BSISO is likely to support the shifting between the modes of ISM-EASM connection.

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Geometric detection of coupling directions by means of inter-system recurrence networks

Cited by 96 publications

References 62 publications

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package

Detection of coupling directions with intersystem recurrence networks

Interconnection between the Indian and the East Asian summer monsoon: Spatial synchronization patterns of extreme rainfall events

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Geometric detection of coupling directions by means of inter-system recurrence networks

Cited by 96 publications

References 62 publications

Unified functional network and nonlinear time series analysis for complex systems science: Thepyunicornpackage

Unified functional network and nonlinear time series analysis for complex systems science: Thepyunicornpackage

Detection of coupling directions with intersystem recurrence networks

Interconnection between the Indian and the East Asian summer monsoon: Spatial synchronization patterns of extreme rainfall events

Contact Info

Product

Resources

About

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package

Unified functional network and nonlinear time series analysis for complex systems science: The`pyunicorn`package