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Incomplete Time Series: Imputation through Genetic Algorithms

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Time Series Analysis, Modeling and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 47))

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Abstract

Uncertainty in time series can appear in many ways, and its analysis can be performed based on different theories. An important problem appears when time series is incomplete since the analyst should impute those observations before any other analysis.

This chapter focuses on designing an imputation method for multiple missing observations in time series through the use of a genetic algorithm (GA), which is designed for replacing these missed observations in the original series. The flexibility of a GA is used for finding an adequate solution to a multicriteria objective, defined as the error between some key properties of the original series and the imputed one. A comparative study between a classical estimation method and our proposal is presented through an example.

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Author information

Authors and Affiliations

  1. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García & César Amilcar López

  2. Universidad de La Sabana, Chía, Colombia

    Dusko Kalenatic

Authors
  1. Juan Carlos Figueroa-García
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  2. Dusko Kalenatic
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  3. César Amilcar López
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Corresponding author

Correspondence to Juan Carlos Figueroa-García .

Editor information

Editors and Affiliations

  1. , Electrical & Computer Engineering, University of Alberta, 9107 - 116 Street, 2nd Floor, ECERF, Edmonton, T6G 2V4, Canada

    Witold Pedrycz

  2. , Graduate Institute of Educational Measur, National Taichung University of Educatio, Administration Building A110, Taichung, Taiwan R.O.C.

    Shyi-Ming Chen

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Figueroa-García, J.C., Kalenatic, D., López, C.A. (2013). Incomplete Time Series: Imputation through Genetic Algorithms. In: Pedrycz, W., Chen, SM. (eds) Time Series Analysis, Modeling and Applications. Intelligent Systems Reference Library, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33439-9_2

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  • DOI: https://doi.org/10.1007/978-3-642-33439-9_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33438-2

  • Online ISBN: 978-3-642-33439-9

  • eBook Packages: EngineeringEngineering (R0)

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