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Neural intuitionistic fuzzy system with justified granularity

  • S.I : Deep learning modelling in real life: (Anomaly Detection, Biomedical, Concept Analysis, Finance, Image analysis, Recommendation)
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Abstract

Fuzzy systems are intensively investigated and extended to construct forecasting models. In particular, intuitionistic fuzzy sets are used to capture higher levels of uncertainty occurring in the modeled data. Neural networks are also used to reflect nonlinearity relationships frequently observed in time series. This paper proposes a new hybrid system merging fuzzy system with neural networks and an advanced optimization technique, the principle of justified granularity. Using this technique, we construct an innovative time-series forecasting model. In the experimental part of the paper, we demonstrate the advantages arising from applying the proposed approach to metal price forecasting. Finally, we provide evidence that the proposed model is competitive with the current state-of-the-art models for the forecasting horizons of one and five days.

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Funding

Funding was provided by Grantová Agentura České Republiky (Grant No. 19-15498S).

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Author notes

  1. Wojciech Froelich, Vladimir Olej and Josef Novotny: These authors have been contributed equally to this work.

Authors and Affiliations

  1. Faculty of Economics and Administration, Science and Research Centre, University of Pardubice, Studentska 84, 532 10, Pardubice, Czech Republic

    Petr Hajek & Vladimir Olej

  2. Institute of Computer Science, University of Silesia, Bedzinska 39, 41-200, Sosnowiec, Poland

    Wojciech Froelich

  3. Faculty of Economics and Administration, Institute of Business Economics and Management, University of Pardubice, Studentska 84, 532 10, Pardubice, Czech Republic

    Josef Novotny

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  1. Petr Hajek
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  2. Wojciech Froelich
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Correspondence to Petr Hajek.

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Hajek, P., Froelich, W., Olej, V. et al. Neural intuitionistic fuzzy system with justified granularity. Neural Comput & Applic 34, 19423–19439 (2022). https://doi.org/10.1007/s00521-022-07504-x

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