Home Itens selecionados Provedores de dados OAI Créditos Sobre Ajuda

			Busca avançada
	Provedor de dados ArchiMer (2) Autor Bigand, Andre (2) Lefebvre, Alain (2) Phan, Thi-thu-hong (2) Poisson Caillault, Emilie (1) Poisson-caillault, Emilie (1) Palavra-chave Missing data (2) Similarity measures (2) Adaptive Feature Based DTW (AF-BDTW) (1) Derivative DTW (DDTW) (1) Dynamic Time Warping (1) Dynamic Time Warping (DTW) (1) Dynamic Time Warping-D (DTW-D) (1) Imputation (1) Uncorrelated multivariate time series (1) Univariate time series (1) Mais... Tipo do documento Text (2) Ano 2017 (2) País France (2) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última Which DTW Method Applied to Marine Univariate Time Series Imputation ArchiMer Phan, Thi-thu-hong; Poisson-caillault, Emilie; Lefebvre, Alain; Bigand, Andre. Missing data are ubiquitous in any domains of applied sciences. Processing datasets containing missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Therefore, the aim of this paper is to build a framework for filling missing values in univariate time series and to perform a comparison of different similarity metrics used for the imputation task. This allows to suggest the most suitable methods for the imputation of marine univariate time series. In the first step, the missing data are completed on various mono-dimensional time series. To fill a missing sub-sequence (gap) in a time series, we first find the most similar sub-sequence to the sub-sequence before (resp. after) this gap according a... Tipo: Text Palavras-chave: Univariate time series; Missing data; Dynamic Time Warping (DTW); Derivative DTW (DDTW); Dynamic Time Warping-D (DTW-D); Adaptive Feature Based DTW (AF-BDTW); Similarity measures. Ano: 2017 URL: http://archimer.ifremer.fr/doc/00435/54681/56096.pdf DTW-Approach for uncorrelated multivariate time series imputation ArchiMer Phan, Thi-thu-hong; Poisson Caillault, Emilie; Bigand, Andre; Lefebvre, Alain. Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper, we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of recurrent data. This method involves two main steps. Firstly, we find the most similar sub-sequence to the sub-sequence before (resp.after) a gap based on the shape-features extraction and Dynamic Time Warping algorithms. Secondly, we fill in the gap by the next (resp.previous) sub-sequence... Tipo: Text Palavras-chave: Imputation; Uncorrelated multivariate time series; Missing data; Dynamic Time Warping; Similarity measures. Ano: 2017 URL: https://archimer.ifremer.fr/doc/00429/54082/55378.pdf Registros recuperados: 2 Primeira ... 1 ... Última

Empresa Brasileira de Pesquisa Agropecuária - Embrapa Todos os direitos reservados, conforme Lei n° 9.610 Política de Privacidade Área restrita		Embrapa Parque Estação Biológica - PqEB s/n° Brasília, DF - Brasil - CEP 70770-901 Fone: (61) 3448-4433 - Fax: (61) 3448-4890 / 3448-4891 SAC: https://www.embrapa.br/fale-conosco