Experiences in the Application of a Multivariate Method for Imputation of Meteorological Data
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| פורמט: | artículo original |
| סטטוס: | Versión publicada |
| Fecha de Publicación: | 2014 |
| תיאור: | 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 |
| מוסד: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| שפה: | Español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/2068 |
| גישה מקוונת: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/2068 |
| מילת מפתח: | Imputación datos faltantes componentes principales meteorología climatología. Imputation missing data principal components meteorology climatology |