A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components
Autor(a) principal: | |
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Data de Publicação: | 2012 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/361 |
Resumo: | The study and modeling of systems have called the attention of several researchers, who are interested in estimating rules to describe data behavior. However, before proceeding with this estimation, it is necessary to understand the intrinsic features embedded in data. When such features are not correctly analyzed, the model accuracy tends to decrease. A well-known way to perform this analysis is by the study of time series behavior according to their stochastic and deterministic components. Nevertheless, the time series decomposition into these components is not a simple task. In order to address this issue, we conducted a rigorous and well-structured search for scientific papers in different repositories. By analyzing the recovered papers, we drew relevant conclusions such as: which methods are commonly used to decompose time series; the frequency of published papers per year; and the gaps of each method. Moreover, we have also classified the most suitable studies to estimate the determinism and stochastic- ity present in time series. After conducting this study, we concluded the development of methods to decompose time series into stochastic and deterministic components is still an open problem. |
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INFOCOMP: Jornal de Ciência da Computação |
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A Systematic Literature Review on Decomposition Approaches to Estimate Time Series ComponentsTime Series AnalysisSignal DecompositionSystematic Literature ReviewThe study and modeling of systems have called the attention of several researchers, who are interested in estimating rules to describe data behavior. However, before proceeding with this estimation, it is necessary to understand the intrinsic features embedded in data. When such features are not correctly analyzed, the model accuracy tends to decrease. A well-known way to perform this analysis is by the study of time series behavior according to their stochastic and deterministic components. Nevertheless, the time series decomposition into these components is not a simple task. In order to address this issue, we conducted a rigorous and well-structured search for scientific papers in different repositories. By analyzing the recovered papers, we drew relevant conclusions such as: which methods are commonly used to decompose time series; the frequency of published papers per year; and the gaps of each method. Moreover, we have also classified the most suitable studies to estimate the determinism and stochastic- ity present in time series. After conducting this study, we concluded the development of methods to decompose time series into stochastic and deterministic components is still an open problem.Editora da UFLA2012-12-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/361INFOCOMP Journal of Computer Science; Vol. 11 No. 3-4 (2012): September-December, 2012; 31-461982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/361/345Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessRios, Ricardo Araújode Mello, Rodrigo Fernandes2015-07-29T14:06:53Zoai:infocomp.dcc.ufla.br:article/361Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:34.321219INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components |
title |
A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components |
spellingShingle |
A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components Rios, Ricardo Araújo Time Series Analysis Signal Decomposition Systematic Literature Review |
title_short |
A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components |
title_full |
A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components |
title_fullStr |
A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components |
title_full_unstemmed |
A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components |
title_sort |
A Systematic Literature Review on Decomposition Approaches to Estimate Time Series Components |
author |
Rios, Ricardo Araújo |
author_facet |
Rios, Ricardo Araújo de Mello, Rodrigo Fernandes |
author_role |
author |
author2 |
de Mello, Rodrigo Fernandes |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Rios, Ricardo Araújo de Mello, Rodrigo Fernandes |
dc.subject.por.fl_str_mv |
Time Series Analysis Signal Decomposition Systematic Literature Review |
topic |
Time Series Analysis Signal Decomposition Systematic Literature Review |
description |
The study and modeling of systems have called the attention of several researchers, who are interested in estimating rules to describe data behavior. However, before proceeding with this estimation, it is necessary to understand the intrinsic features embedded in data. When such features are not correctly analyzed, the model accuracy tends to decrease. A well-known way to perform this analysis is by the study of time series behavior according to their stochastic and deterministic components. Nevertheless, the time series decomposition into these components is not a simple task. In order to address this issue, we conducted a rigorous and well-structured search for scientific papers in different repositories. By analyzing the recovered papers, we drew relevant conclusions such as: which methods are commonly used to decompose time series; the frequency of published papers per year; and the gaps of each method. Moreover, we have also classified the most suitable studies to estimate the determinism and stochastic- ity present in time series. After conducting this study, we concluded the development of methods to decompose time series into stochastic and deterministic components is still an open problem. |
publishDate |
2012 |
dc.date.none.fl_str_mv |
2012-12-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/361 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/361 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/361/345 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 11 No. 3-4 (2012): September-December, 2012; 31-46 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874741414133760 |