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A novel method based on FTS with both GA-FCM and multifactor BPNN for stock forecasting

Abstract

The fuzzy time series (FTS) model has been proposed for many years, and many researches have been conducted to improve or enhance the model. This study proposed a novel method for stock forecasting, which is based on FTS forecasting with genetic algorithm (GA)-fuzzy C-means (FCM) and multifactor back-propagation neural networks (BPNN). The GA algorithm is utilized to alleviate the FCM’s issue of falling into local optimum in the process of partitioning the universe of discourse and fuzzifying the time series. The multifactor BPNN considers relatively more information to train the neural networks and then forecast new stock index fluctuations. Finally, the proposed method is compared with other previous research methods by using SSECI and TAIEX data to verify the proposed method’s effectiveness and efficiency in forecasting financial time series.

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Funding

Acknowledgements

This study was funded by China National Nature Science Foundation (Nos. 51375429, 51475410) and Zhejiang Natural Science Foundation of China (No. LY13E050010).

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Correspondence to Wenyu Zhang.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Cite this article

Zhang, W., Zhang, S., Zhang, S. et al. A novel method based on FTS with both GA-FCM and multifactor BPNN for stock forecasting. Soft Comput 23, 6979–6994 (2019). https://doi.org/10.1007/s00500-018-3335-2

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Keywords

  • Fuzzy time series
  • Genetic algorithm
  • Fuzzy C-means
  • Back-propagation neural networks