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Generalized Nets Model of Dimensionality Reduction in Time Series

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Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

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Abstract

The paper considers the generalized nets as an extension of Petri nets applied for modeling of the methodology called Symbolic Essential Attributes Approximation (Krawczak and Szkatuła, 2014). SEAA was developed to reduce the dimensionality of multidimensional time series by generating a new nominal representation of the original data series. In general the approach is based on the concept of data series envelopes and essential attributes obtained by a multilayer neural network. The symbolic data series representation - which just describes the compressed representation of the original data series - is obtained via discretization of the real-valued essential attributes. In this paper the generalized nets were used to model the logistic of processes involved in SEAA methodology. First the basic of the theory of generalized nets is introduced, next SEAA methodology processes are modeled via the generalized nets the new model of SEAA.

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Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Maciej Krawczak & Grażyna Szkatuła

  2. Warsaw School of Information Technology, Newelska 6, Warsaw, Poland

    Maciej Krawczak

Authors
  1. Maciej Krawczak
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  2. Grażyna Szkatuła
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Corresponding author

Correspondence to Maciej Krawczak .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

  10. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    S. Zadrozny

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Krawczak, M., Szkatuła, G. (2015). Generalized Nets Model of Dimensionality Reduction in Time Series. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_74

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  • DOI: https://doi.org/10.1007/978-3-319-11310-4_74

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

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