PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Non-Linear Approach for Completing Missing Values in Temporal Databases
Paul Merlin, Antti Sorjamaa, Bertrand Maillet and Amaury Lendasse
European Journal of Economic and Social Systems Volume 22, pp. 99-117, 2009.


The presence of missing data in the underlying time series is a recurrent problem for market models. Such models make it necessary to deal with cylindrical and complete samples. Moreover, many financial databases contain missing values. This paper presents a new method for the missing values recovery. The new method is based on two projection methods: a nonlinear 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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6636
Deposited By:Amaury Lendasse
Deposited On:08 March 2010