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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/45STKNB
Repositorysid.inpe.br/mtc-m21d/2021/12.01.18.15   (restricted access)
Last Update2021:12.01.18.15.56 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2021/12.01.18.15.56
Metadata Last Update2022:04.03.23.14.06 (UTC) administrator
DOI10.1140/epjs/s11734-021-00169-y
ISSN1951-6355
1951-6401
Citation KeyPradoMacaLope:2021:DeDaCo
TitleDetection of data corruption in stationary time series using recurrence microstates probabilities
Year2021
MonthOct.
Access Date2024, May 09
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size6325 KiB
2. Context
Author1 Prado, Thiago de Lima
2 Macau, Elbert Einstein Nehrer
3 Lopes, Sérgio Roberto
Resume Identifier1
2 8JMKD3MGP5W/3C9JGUT
Group1
2 COPDT-CGIP-INPE-MCTI-GOV-BR
Affiliation1 Universidade Federal do Paraná (UFPR)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Universidade Federal do Paraná (UFPR)
Author e-Mail Address1 thiago@fisica.ufpr.br
2 elbert.macau@unifesp.br
3 lopes@fisica.ufpr.br
JournalEuropean Physical Journal: Special Topics
Volume230
Number14/15
Pages2737-2744
Secondary MarkA2_ENGENHARIAS_III A2_CIÊNCIAS_AGRÁRIAS_I B1_MEDICINA_I B1_INTERDISCIPLINAR B1_ENGENHARIAS_IV B1_ENGENHARIAS_I B2_QUÍMICA B2_ENGENHARIAS_II B5_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B5_ASTRONOMIA_/_FÍSICA C_CIÊNCIAS_BIOLÓGICAS_I C_CIÊNCIA_DE_ALIMENTOS C_BIODIVERSIDADE
History (UTC)2021-12-01 18:15:56 :: simone -> administrator ::
2021-12-01 18:15:58 :: administrator -> simone :: 2021
2021-12-01 18:16:17 :: simone -> administrator :: 2021
2022-04-03 23:14:06 :: administrator -> simone :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
AbstractRecurrence 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.
AreaCOMP
Arrangementurlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > Detection of data...
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4. Conditions of access and use
Languageen
Target FilePrado2021_Article_DetectionOfDataCorruptionInSta.pdf
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Visibilityshown
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Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46KUES5
Citing Item Listsid.inpe.br/bibdigital/2022/04.03.23.11 3
sid.inpe.br/mtc-m21/2012/07.13.14.45.07 1
DisseminationWEBSCI; PORTALCAPES.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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