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| Registros recuperados : 9 | |
| Registros recuperados : 9 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
19/08/2011 |
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Data da última atualização: |
06/01/2012 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
C - 0 |
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Autoria: |
NUNES, S. A.; ROMANI, L. A. S.; AVILA, A. M. H.; TRAINA JUNIOR, C.; SOUSA, E. P. M. de; TRAINA, A. J. M. |
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Afiliação: |
SANTIAGO AUGUSTO NUNES, ICMC/USP; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; ANA M. H. AVILA, Cepagri/Unicamp; CAETANO TRAINA JUNIOR, ICMC/USP; ELAINE P. M. DE SOUSA, ICMC/USP; AGMA J. M. TRAINA, ICMC/USP. |
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Título: |
Fractal-based analysis to identify trend changes in multiple climate time series. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
Journal of Information and Data Management, Belo Horizonte, v. 2, n. 1, p. 51-57, Feb. 2011. |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract. In the last few decades, huge amounts of climate data have been gathered and stored by several institutions. The analysis of these data has become an important task due to worldwide climate changes and the consequent social and economic effects. In this work, we propose an approach to analyzing multiple climate time series in order to identify intrinsic temporal patterns and trend changes. By dealing with multiple time series as multidimensional data streams and combining fractal-based analysis with clustering, we can integrate different climate variables and discover general behavior changes over time. |
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Palavras-Chave: |
Clusterização; Dados climáticos; Mineração de dados; Séries temporais. |
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Thesaurus NAL: |
Climate; Cluster analysis; Meteorology and climatology; Time series analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/40113/1/fractal105-389-1-PB.pdf
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Marc: |
LEADER 01481naa a2200277 a 4500
001 1898365
005 2012-01-06
008 2011 bl uuuu u00u1 u #d
100 1 $aNUNES, S. A.
245 $aFractal-based analysis to identify trend changes in multiple climate time series.$h[electronic resource]
260 $c2011
520 $aAbstract. In the last few decades, huge amounts of climate data have been gathered and stored by several institutions. The analysis of these data has become an important task due to worldwide climate changes and the consequent social and economic effects. In this work, we propose an approach to analyzing multiple climate time series in order to identify intrinsic temporal patterns and trend changes. By dealing with multiple time series as multidimensional data streams and combining fractal-based analysis with clustering, we can integrate different climate variables and discover general behavior changes over time.
650 $aClimate
650 $aCluster analysis
650 $aMeteorology and climatology
650 $aTime series analysis
653 $aClusterização
653 $aDados climáticos
653 $aMineração de dados
653 $aSéries temporais
700 1 $aROMANI, L. A. S.
700 1 $aAVILA, A. M. H.
700 1 $aTRAINA JUNIOR, C.
700 1 $aSOUSA, E. P. M. de
700 1 $aTRAINA, A. J. M.
773 $tJournal of Information and Data Management, Belo Horizonte$gv. 2, n. 1, p. 51-57, Feb. 2011.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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