The reliability of recurrence network analysis is influenced by the observability properties of the recorded time series

Leonardo L. Portes, Arthur N. Montanari, Debora C. Correa, Michael Small, Luis A. Aguirre

Research output: Contribution to journalArticle

Abstract

Recurrence network analysis (RNA) is a remarkable technique for the detection of dynamical transitions in experimental applications. However, in practical experiments, often only a scalar time series is recorded. This requires the state-space reconstruction from this single time series which, as established by embedding and observability theory, is shown to be hampered if the recorded variable conveys poor observability. In this work, we investigate how RNA metrics are impacted by the observability properties of the recorded time series. Following the framework of Zou et al. [Chaos 20, 043130 (2010)], we use the Rössler and Duffing-Ueda systems as benchmark models for our study. It is shown that usually RNA metrics perform badly with variables of poor observability as for recurrence quantification analysis. An exception is the clustering coefficient, which is rather robust to observability issues. Along with its efficacy to detect dynamical transitions, it is shown to be an efficient tool for RNA - especially when no prior information of the variable observability is available.

Original languageEnglish
Article number083101
JournalChaos
Volume29
Issue number8
DOIs
Publication statusPublished - 1 Aug 2019

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network analysis
Observability
Network Analysis
Electric network analysis
Recurrence
Time series
Dynamical Transition
Recurrence Quantification Analysis
Metric
Clustering Coefficient
Prior Information
Chaos theory
embedding
Exception
chaos
Efficacy
Chaos
State Space
Scalar
Benchmark

Cite this

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title = "The reliability of recurrence network analysis is influenced by the observability properties of the recorded time series",
abstract = "Recurrence network analysis (RNA) is a remarkable technique for the detection of dynamical transitions in experimental applications. However, in practical experiments, often only a scalar time series is recorded. This requires the state-space reconstruction from this single time series which, as established by embedding and observability theory, is shown to be hampered if the recorded variable conveys poor observability. In this work, we investigate how RNA metrics are impacted by the observability properties of the recorded time series. Following the framework of Zou et al. [Chaos 20, 043130 (2010)], we use the R{\"o}ssler and Duffing-Ueda systems as benchmark models for our study. It is shown that usually RNA metrics perform badly with variables of poor observability as for recurrence quantification analysis. An exception is the clustering coefficient, which is rather robust to observability issues. Along with its efficacy to detect dynamical transitions, it is shown to be an efficient tool for RNA - especially when no prior information of the variable observability is available.",
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The reliability of recurrence network analysis is influenced by the observability properties of the recorded time series. / Portes, Leonardo L.; Montanari, Arthur N.; Correa, Debora C.; Small, Michael; Aguirre, Luis A.

In: Chaos, Vol. 29, No. 8, 083101, 01.08.2019.

Research output: Contribution to journalArticle

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AU - Portes, Leonardo L.

AU - Montanari, Arthur N.

AU - Correa, Debora C.

AU - Small, Michael

AU - Aguirre, Luis A.

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