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An Empirical Mode Decomposition Based Method to Synthesize Ensemble Multidimensional Gaussian Neuro-Fuzzy Models in Financial Forecasting

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Data Stream Mining & Processing (DSMP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1158))

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Abstract

Time series arise in different fields of the economy and forecasting of them is important part of decision making. However, their intrinsic complexity and nonlinear behavior makes prediction in that field a challenging task. Hybrid artificial intelligence models are among the most powerful tools in handling such complex dynamics. This paper introduces a novel model which uses the empirical mode decomposition as a denoising and decomposition framework for the ensemble multidimensional Gaussian based neuro-fuzzy model in order to achieve better accuracy. The computational experimental results show clear advantages of the proposed approach - better prediction accuracy, faster error decay with preserving of the generalization abilities and reasonable computational overhead.

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Authors and Affiliations

  1. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Alexander Vlasenko & Olena Vynokurova

  2. Simon Kuznets Kharkiv National University of Economics, Kharkiv, Ukraine

    Nataliia Vlasenko

  3. GeoGuard, Kharkiv/Lviv, Ukraine

    Olena Vynokurova & Dmytro Peleshko

Authors
  1. Alexander Vlasenko
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  2. Nataliia Vlasenko
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  3. Olena Vynokurova
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  4. Dmytro Peleshko
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Corresponding author

Correspondence to Alexander Vlasenko .

Editor information

Editors and Affiliations

  1. Department of Informatics, Univerzita Jana Evangelisty Purkyně v Ústí nad Labem, Ústí nad Labem, Czech Republic

    Sergii Babichev

  2. GeoGuard, Kharkiv, Ukraine

    Dmytro Peleshko

  3. Kharkiv National University of Radio Electronics, Kharkiv, Ukraine

    Olena Vynokurova

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Vlasenko, A., Vlasenko, N., Vynokurova, O., Peleshko, D. (2020). An Empirical Mode Decomposition Based Method to Synthesize Ensemble Multidimensional Gaussian Neuro-Fuzzy Models in Financial Forecasting. In: Babichev, S., Peleshko, D., Vynokurova, O. (eds) Data Stream Mining & Processing. DSMP 2020. Communications in Computer and Information Science, vol 1158. Springer, Cham. https://doi.org/10.1007/978-3-030-61656-4_9

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  • DOI: https://doi.org/10.1007/978-3-030-61656-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61655-7

  • Online ISBN: 978-3-030-61656-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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