@Article{RamosBuPoGoMaKuMa:2017:ReMeCo,
               author = "Ramos, Ant{\^o}nio M{\'a}rio de Torres and Builes-Jaramillo, 
                         Alejandro and Poveda, Germ{\'a}n and Goswami, Bedartha and Macau, 
                         Elbert Einstein Nehrer and Kurths, J{\"u}rgen and Marwan, 
                         Norbert",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidad Nacional de Colombia} and {Universidad Nacional de 
                         Colombia} and {Potsdam Institute for Climate Impact Research} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Potsdam 
                         Institute for Climate Impact Research} and {Potsdam Institute for 
                         Climate Impact Research}",
                title = "Recurrence measure of conditional dependence and applications",
              journal = "Physical Review E",
                 year = "2017",
               volume = "95",
               number = "5",
                pages = "052206",
                 note = "{Setores de Atividade: Pesquisa e desenvolvimento 
                         cient{\'{\i}}fico.}",
             keywords = "Din{\^a}mica N{\~a}o Linear, An{\'a}lise de s{\'e}ries 
                         temporais, Causalidade, Determina{\c{c}}{\~a}o de 
                         Intera{\c{c}}{\~o}es, Gr{\'a}fico de Recorr{\^e}ncia.",
             abstract = "Identifying causal relations from observational data sets has 
                         posed great challenges in data-driven causality inference studies. 
                         One of the successful approaches to detect direct coupling in the 
                         information theory framework is transfer entropy. However, the 
                         core of entropy-based tools lies on the probability estimation of 
                         the underlying variables. Herewe propose a data-driven approach 
                         for causality inference that incorporates recurrence plot features 
                         into the framework of information theory. We define it as the 
                         recurrence measure of conditional dependence (RMCD), and we 
                         present some applications. The RMCD quantifies the causal 
                         dependence between two processes based on joint recurrence 
                         patterns between the past of the possible driver and present of 
                         the potentially driven, excepting the contribution of the 
                         contemporaneous past of the driven variable. Finally, it can 
                         unveil the time scale of the influence of the sea-surface 
                         temperature of the Pacific Ocean on the precipitation in the 
                         Amazonia during recent major droughts.",
                  doi = "10.1103/physreve.95.052206",
                  url = "http://dx.doi.org/10.1103/physreve.95.052206",
                 issn = "1539-3755",
                label = "lattes: 0793627832164040 5 RamosBuPoGoMaKuMa:2017:ReMeCo",
             language = "en",
           targetfile = "ramos_recurrence.pdf",
        urlaccessdate = "25 abr. 2024"
}