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Chaotic Interval Type-2 Fuzzy Neuro-oscillatory Network (CIT2-FNON) for Worldwide 129 Financial Products Prediction

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Abstract

In this paper, the author proposes an innovative Chaotic Interval Type-2 Fuzzy Neuro-oscillatory Network (CIT2-FNON) for worldwide financial prediction. Inspired by the author’s original work on Lee-oscillator—a chaotic discrete-time neural oscillator with profound transient-chaotic property, CIT2-FNON provides: (1) effective modeling of Interval Type-2 Fuzzy Logic with Chaotic Transient-Fuzzy Membership Function (CTFMF); and (2) time-series recurrent neural network training and prediction with Chaotic Bifurcation Transfer Function (CBTF). Different from the contemporary research on Type-2 Fuzzy Logic Systems (T2FLS) which mainly focus on the R&D of the Interval Type-2 Fuzzy Logic (IT2FL)—a simplified version of T2FLS due to its computational complexity, the main innovation of this paper include: (1) the Chaotic Type-2 Transient-Fuzzy Logic (CT2TFL) proposed in this paper provides a truly T2FLS with remarkable chaotic transient-fuzzy property to resolve the computational complexity problem; and (2) different from contemporary fuzzy-neuro systems which focus on the integration of fuzzy logic and neural networks as separated functional modules, the CIT2-FNON introduced in this paper is constructed by Lee-oscillators which serves as “transient-fuzzy input neurons” of the recurrent network and effectively converts it into CT2TFL system. In other words, the chaotic transient-fuzzification process is actually part of the neural model of the CIT2-FNON. From the implementation perspective, CIT2-FNON is integrated with 2048-trading day time-series financial data and Top-10 major financial signals as financial fuzzy signals (FFS) for the real-time prediction of 129 worldwide financial products.

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Acknowledgements

The author wishes to thank Forex.com and Avatrade.com for the provision of historical and real-time 120 + financial product data over their MT4 R&D and trading platforms. The author also wishes to thank Quantum Finance Forecast Center of UIC for the R&D supports and the provision of the channel and platform qffc.org for worldwide system testing and evaluation. This paper and research Project is supported by Research Grant R201948 of Beijing Normal University-Hong Kong Baptist University United International College (UIC).

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  1. Division of Science and Technology, BNU-HKBU United International College, Zhuhai, Guangdong, China

    Raymond S. T. Lee

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Correspondence to Raymond S. T. Lee.

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Lee, R.S.T. Chaotic Interval Type-2 Fuzzy Neuro-oscillatory Network (CIT2-FNON) for Worldwide 129 Financial Products Prediction. Int. J. Fuzzy Syst. 21, 2223–2244 (2019). https://doi.org/10.1007/s40815-019-00688-w

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