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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,
,
2009.
AbstractThe 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. [Edit]
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