期刊
NEUROCOMPUTING
卷 73, 期 7-9, 页码 1103-1108出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2009.11.019
关键词
Missing value completion; Self-organizing maps; Empirical orthogonal function
In this paper, a new method for the determination of missing values in temporal databases is presented. It is based on a robust version of a nonlinear classification algorithm called self-organizing maps and it consists of a combination of two classifications in order to take advantage of spatial as well as temporal dependencies of the dataset. This double classification leads to a significant improvement of the estimation of the missing values. An application of the missing value imputation for hedge fund returns is presented. (C) 2010 Elsevier B.V. All rights reserved.
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