Detection of data corruption in stationary time series using recurrence microstates probabilities

Autor(es)

Prado, Thiago de Lima ^[1] ; Macau, Elbert Einstein Nehrer ^{[2, 3]} ; Lopes, Sergio Roberto ^[1]

Afiliação do(s) autor(es)

^[1] Univ Fed Parana, Dept Fis, BR-81531980 Curitiba, PR - Brazil

^[2] Univ Fed Sao Paulo, Inst Ciencia Tecnol ICT, BR-12231280 Sao Jose Dos Campos, SP - Brazil

^[3] Inst Nacl Pesquisas Espaciais, Lab Associado Comp & Math Aplicads, BR-12227010 Sao Jose Dos Campos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: European Physical Journal-Special Topics; v. 230, n. 14-15, p. 2737-2744, OCT 2021.
• Citações Web of Science: 1

Resumo

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. (AU)

• Processo FAPESP: 15/50122-0 - Fenômenos dinâmicos em redes complexas: fundamentos e aplicações
• Beneficiário: Elbert Einstein Nehrer Macau
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/03211-6 - Dinâmica não linear
• Beneficiário: Iberê Luiz Caldas
• Modalidade de apoio: Auxílio à Pesquisa - Temático