Experiences in the Application of a Multivariate Method for Imputation of Meteorological Data
Guardado en:
| Údar: | |
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| Formáid: | artículo original |
| Stádas: | Versión publicada |
| Fecha de Publicación: | 2014 |
| Cur Síos: | Different statistical methods and techniques have been proposed for dealing with missing data. This study discusses the application of the principal components approach for filling hourly meteorological data. In order to test the possibilities that this approach offers, preliminary tests were conducted by random removal of real data in time series. Missing data were predicted using a principal-components algorithm. The results show that this method could predict the missing information with an mean absolute error that is around 1ºC in most of the cases. |
| País: | Portal de Revistas TEC |
| Institiúid: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| Teanga: | Español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/2068 |
| Rochtain Ar Líne: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/2068 |
| Palabra clave: | Imputación datos faltantes componentes principales meteorología climatología. Imputation missing data principal components meteorology climatology |