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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2013: Artificial Intelligence and Soft Computing pp 345–352Cite as

Modelling Financial High Frequency Data Using Ordered Fuzzy Numbers

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7894))

Abstract

The goal of the paper is to present an original concept of representation of financial high frequency data using Ordered Fuzzy Numbers. This approach allows the transition from high frequency data (e.g. ticks, minutes) to lower frequency data (e.g. daily) while maintaining more information about price movement at assumed time interval than using the popular price charts (e.g. Japanese Candlestick chart). The financial data are modeled using Ordered Fuzzy Numbers called further by Ordered Fuzzy Candlesticks. The use of them allows also modeling uncertainty associated with financial data. How to construct and use Ordered Fuzzy Candlesticks are presented in the main part of this paper.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Computational Intelligence Department, Cracow University of Technology, Warszawska 24, 31-155, Cracow, Poland

    Adam Marszałek & Tadeusz Burczyński

  2. Institute of Computational Mechanics and Engineering, Silesian University of Technology, Konarskiego 18A, 44-100, Gliwice, Poland

    Tadeusz Burczyński

Authors
  1. Adam Marszałek
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  2. Tadeusz Burczyński
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2013 Springer-Verlag Berlin Heidelberg

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Marszałek, A., Burczyński, T. (2013). Modelling Financial High Frequency Data Using Ordered Fuzzy Numbers. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38658-9_31

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  • DOI: https://doi.org/10.1007/978-3-642-38658-9_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38657-2

  • Online ISBN: 978-3-642-38658-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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