@Article{PradoMacaLope:2021:DeDaCo,
               author = "Prado, Thiago de Lima and Macau, Elbert Einstein Nehrer and Lopes, 
                         S{\'e}rgio Roberto",
          affiliation = "{Universidade Federal do Paran{\'a} (UFPR)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         do Paran{\'a} (UFPR)}",
                title = "Detection of data corruption in stationary time series using 
                         recurrence microstates probabilities",
              journal = "European Physical Journal: Special Topics",
                 year = "2021",
               volume = "230",
               number = "14/15",
                pages = "2737--2744",
                month = "Oct.",
             abstract = "Recurrence microstates can be used to analyze many properties of 
                         stationary states of stochastic and deterministic time series, 
                         including the level of correlation of stochastic signals. Here, we 
                         show how artificially inserted data (data that does not belong to 
                         a original stationary signal) may be detected using recurrence 
                         microstates statistics. We show that the method is sensitive 
                         enough to detect the breaking of the stationary signal even when 
                         the corrupted inserted data span into the same domain of the 
                         original data. Examples of our analyses are applied to two 
                         numerically generated time series of dynamical systems, namely the 
                         logistic map, and the Lorenz equations. Finally to show results 
                         applied to experimental time series, we analyze a digital audio 
                         signal of a human speech.",
                  doi = "10.1140/epjs/s11734-021-00169-y",
                  url = "http://dx.doi.org/10.1140/epjs/s11734-021-00169-y",
                 issn = "1951-6355 and 1951-6401",
             language = "en",
           targetfile = "Prado2021_Article_DetectionOfDataCorruptionInSta.pdf",
        urlaccessdate = "30 abr. 2024"
}