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  Optimizing the detection of nonstationary signals by using recurrence analysis

de Lima Prado, T., dos Santos Lima, G. Z., Lobao-Soares, B., do Nascimento, G. C., Corso, G., Fontenele-Araujo, J., Kurths, J., Lopes, S. R. (2018): Optimizing the detection of nonstationary signals by using recurrence analysis. - Chaos, 28, Art. 085703.
https://doi.org/10.1063/1.5022154

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_22831 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_22831_2
Genre: Journal Article

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de Lima Prado, T.1, Author
dos Santos Lima, G. Z.1, Author
Lobao-Soares, B.1, Author
do Nascimento, G. C.1, Author
Corso, G.1, Author
Fontenele-Araujo, J.1, Author
Kurths, Jürgen2, Author              
Lopes, S. R.1, Author
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1Potsdam Institute for Climate Impact Research and Cooperation Partners, ou_persistent13              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Dates: 2018
 Publication Status: Finally published
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 Identifiers: DOI: 10.1063/1.5022154
PIKDOMAIN: Transdisciplinary Concepts & Methods - Research Domain IV
eDoc: 8312
Working Group: Development of advanced time series analysis techniques
Working Group: Network- and machine-learning-based prediction of extreme events
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Title: Chaos
Source Genre: Journal
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Pages: - Volume / Issue: 28 (Art. 085703) Sequence Number: - Start / End Page: - Identifier: -