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

Authors

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;

/persons/resource/Juergen.Kurths

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). doi:10.1063/1.5022154.

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

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