Home Itens selecionados Provedores de dados OAI Créditos Sobre Ajuda

			Busca avançada
	Provedor de dados 5 (2) 31 (1) Autor Lefebvre, Alain (2) Phan, Thi-thu-hong (2) Bigand, Andre (1) Bigand, André (1) Brown, Jason P. (1) English, Burton C. (1) Jensen, Kimberly L. (1) Lambert, Dayton M. (1) Menard, R. Jamey (1) Poisson Caillault, Emilie (1) Poisson Caillault, Émilie Poisson (1) Register, D. Lane (1) Mais... Palavra-chave Imputation (3) Missing data (2) County Business Pattern data (1) DTW (1) Dynamic Time Warping (1) Research Methods/ Statistical Methods (1) Similarity (1) Similarity measures (1) Uncorrelated multivariate time series (1) Univariate time series (1) Mais... Tipo do documento Text (2) Presentation (1) Ano 2017 (2) 2012 (1) País France (2) United States (1) Idioma Inglês (3)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 3 Primeira ... 1 ... Última Dynamic Time Warping-based imputation for univariate time series data 5 Phan, Thi-thu-hong; Poisson Caillault, Émilie Poisson; Lefebvre, Alain; Bigand, André. Time series with missing values occur in almost any domain of applied sciences. Ignoring missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). This paper proposes an approach to fill in large gap(s) within time series data under the assumption of effective information. To obtain the imputation of missing values, we find the most similar sub-sequence to the sub-sequence before (resp. after) the missing values, then complete the gap by the next (resp. previous) sub-sequence of the most similar one. Dynamic Time Warping algorithm is applied to compare sub-sequences, and combined with the shape-feature extraction algorithm for reducing insignificant solutions. Eight well-known and real-world data... Tipo: Text Palavras-chave: Imputation; Missing data; Univariate time series; DTW; Similarity. Ano: 2017 URL: http://archimer.ifremer.fr/doc/00396/50696/51387.pdf DTW-Approach for uncorrelated multivariate time series imputation 5 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 Imputation of Suppressed CBP Employment Records 31 Register, D. Lane; Lambert, Dayton M.; English, Burton C.; Jensen, Kimberly L.; Menard, R. Jamey; Brown, Jason P.. Suppression of employment records in the US County Business Patterns (CBP) data sets constrains the detail of new methods and recent advances in the analysis of the geographic distribution of firms and employment. Data sets created by imputation procedures can be purchased, but cost often puts them beyond the reach of many research budgets. Fortunately, methods exist whereby researchers can impute suppressed employment records. A comparison of these procedures is necessary to assess the accuracy and flexibility of each. Tipo: Presentation Palavras-chave: Imputation; County Business Pattern data; Research Methods/ Statistical Methods. Ano: 2012 URL: http://purl.umn.edu/124039 Registros recuperados: 3 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