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Optimization of Ensemble Neural Networks with Type-1 and Interval Type-2 Fuzzy Integration for Forecasting the Taiwan Stock Exchange

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Advances in Data Analysis with Computational Intelligence Methods

Part of the book series: Studies in Computational Intelligence ((SCI,volume 738))

Abstract

This paper describes an optimization method based on particle swarm optimization for ensemble neural networks with type-1 and type-2 fuzzy aggregation for forecasting complex time series. The time series that was considered in this paper to compare the hybrid approach with traditional methods is the Taiwan Stock Exchange (TAIEX), and the results shown are for the optimization of the structure of the ensemble neural network with type-1 and type-2 fuzzy integration. Simulation results show that ensemble approach produces good prediction of the Taiwan Stock Exchange.

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Acknowledgements

We would like to express our gratitude to the CONACYT, Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, México, USA

    Martha Pulido, Patricia Melin & Olivia Mendoza

Authors
  1. Martha Pulido
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  2. Patricia Melin
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  3. Olivia Mendoza
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Corresponding author

Correspondence to Patricia Melin .

Editor information

Editors and Affiliations

  1. Department of Medicine, Division of Nephrology and Hypertension, University of Louisville, Louisville, Kentucky, USA

    Adam E Gawęda

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

    Janusz Kacprzyk

  3. Institute of Computational Intelligence, Department of Mechanical Engineering and Computer Science, Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  4. School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, Oklahoma, USA

    Gary G. Yen

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Pulido, M., Melin, P., Mendoza, O. (2018). Optimization of Ensemble Neural Networks with Type-1 and Interval Type-2 Fuzzy Integration for Forecasting the Taiwan Stock Exchange. In: Gawęda, A., Kacprzyk, J., Rutkowski, L., Yen, G. (eds) Advances in Data Analysis with Computational Intelligence Methods. Studies in Computational Intelligence, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-319-67946-4_6

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  • DOI: https://doi.org/10.1007/978-3-319-67946-4_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67945-7

  • Online ISBN: 978-3-319-67946-4

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