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, Jürgen; Lopes, S. R.

doi:10.1063/1.5022154

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Optimizing the detection of nonstationary signals by using recurrence analysis

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de Lima Prado, T.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

dos Santos Lima, G. Z.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Lobao-Soares, B.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

do Nascimento, G. C.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Corso, G.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

Fontenele-Araujo, J.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

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Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

Lopes, S. R.
Potsdam Institute for Climate Impact Research and Cooperation Partners;

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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

Cite as: https://publications.pik-potsdam.de/pubman/item/item_22831

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