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A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting

Author

Listed:
  • Alexander Vlasenko

    (Department of Artificial Intelligence, Faculty of Computer Science, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine)

  • Nataliia Vlasenko

    (Department of Informatics and Computer Engineering, Faculty of Economic Informatics, Simon Kuznets Kharkiv National University of Economics, 61166 Kharkiv, Ukraine)

  • Olena Vynokurova

    (Information Technology Department, IT Step University, 79019 Lviv, Ukraine
    Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine)

  • Yevgeniy Bodyanskiy

    (Department of Artificial Intelligence, Faculty of Computer Science, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine
    Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine)

  • Dmytro Peleshko

    (Information Technology Department, IT Step University, 79019 Lviv, Ukraine)

Abstract

Neuro-fuzzy models have a proven record of successful application in finance. Forecasting future values is a crucial element of successful decision making in trading. In this paper, a novel ensemble neuro-fuzzy model is proposed to overcome limitations and improve the previously successfully applied a five-layer multidimensional Gaussian neuro-fuzzy model and its learning. The proposed solution allows skipping the error-prone hyperparameters selection process and shows better accuracy results in real life financial data.

Suggested Citation

  • Alexander Vlasenko & Nataliia Vlasenko & Olena Vynokurova & Yevgeniy Bodyanskiy & Dmytro Peleshko, 2019. "A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting," Data, MDPI, vol. 4(3), pages 1-11, August.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:3:p:126-:d:260463
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    Cited by:

    1. Darrold Cordes & Shahram Latifi & Gregory M. Morrison, 2022. "Systematic literature review of the performance characteristics of Chebyshev polynomials in machine learning applications for economic forecasting in low-income communities in sub-Saharan Africa," SN Business & Economics, Springer, vol. 2(12), pages 1-33, December.

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