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Fuzzy Regression with Quadratic Programming: An Application to Financial Data

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Intelligent Data Engineering and Automated Learning – IDEAL 2006 (IDEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4224))

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Abstract

The fuzzy approach to regression has been traditionally considered as a problem of linear programming. In this work, we introduce a variety of models founded on quadratic programming together with a set of indices useful to check the quality of the obtained results. In order to test the validness of our proposal, we have done an empirical study and we have applied the models in a case with financial data: the Chilean COPEC Company stock price.

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Author information

Authors and Affiliations

  1. UTEM – Santiago de Chile, Chile

    Sergio Donoso

  2. Dept. of Computer Science and A. I., University of Granada, 18071, Granada, Spain

    Nicolás Marín & M. Amparo Vila

Authors
  1. Sergio Donoso
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  2. Nicolás Marín
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  3. M. Amparo Vila
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Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior, GICAP Research Group, Universidad de Burgo, Calle Francisco de Vitoria S/N, Edifico C, Campus Vena, 09006, Burgos, Spain

    Emilio Corchado

  2. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  3. Department of Information Systems and Computation, Technical University of Valencia, Camino de Vera, Valencia, Spain

    Vicente Botti

  4. University of West Scotland, PA1 2BE, Paisley, Scotland

    Colin Fyfe

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© 2006 Springer-Verlag Berlin Heidelberg

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Donoso, S., Marín, N., Vila, M.A. (2006). Fuzzy Regression with Quadratic Programming: An Application to Financial Data. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_155

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  • DOI: https://doi.org/10.1007/11875581_155

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45485-4

  • Online ISBN: 978-3-540-45487-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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