Improving reconstruction of time-series based in singular spectrum analysis: a segmentation approach

Página do item simplificado
dc.creatorMichel Carlo Rodrigues Leles
dc.creatorJoão Pedro Hallack Sansão
dc.creatorLeonardo Amaral Mozelli
dc.creatorHomero Nogueira Guimarães
dc.date.accessioned2025-04-24T17:40:17Z
dc.date.accessioned2025-09-08T22:50:41Z
dc.date.available2025-04-24T17:40:17Z
dc.date.issued2018
dc.identifier.doihttps://doi.org/10.1016/j.dsp.2017.10.025
dc.identifier.issn1051-2004
dc.identifier.urihttps://hdl.handle.net/1843/81814
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofDigital signal processing
dc.rightsAcesso Restrito
dc.subjectAnálise espectral
dc.subject.otherSingular spectrum analysis
dc.subject.otherNon-stationary signals
dc.subject.otherSegmentation
dc.titleImproving reconstruction of time-series based in singular spectrum analysis: a segmentation approach
dc.typeArtigo de periódico
local.citation.epage76
local.citation.spage63
local.citation.volume77
local.description.resumoSingular Spectrum Analysis (SSA) is a powerful non-parametric framework to analysis and enhancement of time-series. SSA may be capable of decomposing a time-series into its meaningful components: trends, oscillations and noise. However, if the signal under analysis is non-stationary, with its spectrum spreading and varying in time, the reliability of the reconstruction is guaranteed only when many elementary matrices are used. As a consequence, the capability to discriminate dominant structures from time-series may be impaired. To circumvent this issue, a new method, called overlap-SSA (ov-SSA), is proposed for segmentation, analysis and reconstruction of long-term and/or non-stationary signals. The raw time series is divided into smaller, consecutive and overlapping segments, and standard SSA procedures are applied to each segment with the resulting series being concatenated. This variation of SSA seeks to: improve reconstruction and component separability for non-stationary time-series; enable the analysis for large datasets, avoiding the issues of concatenation of many segments; and present some benefits of the segmentation in terms of better time–frequency characterization. These advantages are illustrated in several synthetic and experimental datasets.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S105120041730252X

Arquivos

Licença do pacote

Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
License.txt
Tamanho:
1.99 KB
Formato:
Plain Text
Descrição:
Baixar

Coleções

Artigo de Periódico

Logo UFMG