Abstract
In This Paper, an Improved Methodology for the Determination of Missing Values in a Spatiotemporal Database is Presented. This Methodology Performs Denoising Projection in Order to Accurately Fill the Missing Values in the Database. the Improved Methodology is Called Empirical Orthogonal Functions (EOF) Pruning, and It is based on an Original Linear Projection Method Called Empirical Orthogonal Functions (EOF). the Experiments Demonstrate the Performance of the Improved Methodology and Present a Comparison with the Original EOF and with a Widely Used Optimal Interpolation Method Called Objective Analysis. © Springer Science + Business Media B.v. 2009.
Recommended Citation
A. Sorjamaa et al., "An Improved Methodology for Filling Missing Values in Spatiotemporal Climate Data Set," Computational Geosciences, vol. 14, no. 1, pp. 55 - 64, Springer, Jan 2010.
The definitive version is available at https://doi.org/10.1007/s10596-009-9132-3
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Empirical orthogonal functions; EOF; Missing value problem; Selection of singular values; Tanganyika Lake
International Standard Serial Number (ISSN)
1420-0597
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Jan 2010
