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A Non-Linear Approach for Completing Missing Values in Temporal Databases

Antti Sorjamaa (), Paul Merlin (), Bertrand Maillet and Amaury Lendasse ()
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Antti Sorjamaa: Helsinki University of Technology
Paul Merlin: A.A.Advisors, Variances and Université Paris I, Pantheon-Sorbonne
Amaury Lendasse: Helsinki University of Technology

European Journal of Economic and Social Systems, 2009, vol. 22, issue 1, 99-117

Abstract: The presence of missing data in the underlying time-series is a recurrent problem for market models. Such models impose to deal with cylindrical and complete samples. This paper presents a new procedure for the missing values recovery. The proposed method is based on two projection algorithms: a non-linear one (Self-Organizing Maps) and a linear one (Empirical Orthogonal Functions). The presented global methodology combines the advantages of both methods to get accurate approximations for the missing values. The methods are applied to three financial datasets.

Keywords: Missing Values; Self-Organizing Maps; Empirical Orthogonal Functions (search for similar items in EconPapers)
JEL-codes: C22 C58 G23 (search for similar items in EconPapers)
Date: 2009
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