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

Author

Listed:
  • Sorjamaa, Antti

    (Helsinki University of Technology)

  • Merlin, Paul

    (A.A.Advisors, Variances and Université Paris I, Pantheon-Sorbonne)

  • Maillet, Bertrand

    (A.A.Advisors, Variances and Université Paris I, Pantheon-Sorbonne)

  • Lendasse, Amaury

    (Helsinki University of Technology)

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.

Suggested Citation

  • Sorjamaa, Antti & Merlin, Paul & Maillet, Bertrand & Lendasse, Amaury, 2009. "A Non-Linear Approach for Completing Missing Values in Temporal Databases," European Journal of Economic and Social Systems, Lavoisier, vol. 22(1), pages 99-117.
    • Handle: RePEc:ris:ejessy:0064
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    More about this item

    Keywords

    Missing Values; Self-Organizing Maps; Empirical Orthogonal Functions;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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