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Nonlinear Dynamics

, Volume 94, Issue 2, pp 1429–1446 | Cite as

Two-factor high-order fuzzy-trend FTS model based on BSO-FCM and improved KA for TAIEX stock forecasting

  • Wenyu Zhang
  • Shixiong Zhang
  • Shuai Zhang
Original Paper
  • 183 Downloads

Abstract

Fuzzy time series has been an effective and attractive forecasting model for solving the problem of stock index forecasting. In particular, fuzzy-trend fuzzy time series models have been proposed recently to address complex cases and perform well in terms of forecasting accuracy. Nonetheless, they have just explored the two-factor second-order forecasting but cannot satisfy the complex stock system. In this study, we proposed a new two-factor high-order fuzzy-trend fuzzy time series model to explore the more complex situation on the TAIEX stock index forecasting. We presented the backtracking search optimization-fuzzy c-means method to obtain the optimal intervals of the data sets. In addition, an improved kidney-inspired algorithm is employed to integrate the high-order forecasting values. The proposed model shows outstanding forecasting accuracy than the benchmark methods on the TAIEX. Besides, we combined two other stock indexes (NASDAQ and the Dow Jones) as the secondary factors, respectively. It provides a useful method for two-factor high-order fuzzy-trend fuzzy time series stock index forecasting.

Keywords

Fuzzy-trend fuzzy time series Two-factor high-order Backtracking search optimization Kidney-inspired algorithm TAIEX forecast 

Notes

Acknowledgements

This work has been supported by National Social Science Foundation of China (No. 16ZDA053), National Nature Science Foundation of China (No. 51475410), Zhejiang Nature Science Foundation of China (No. LY17E050010).

Compliance with ethical standards

Conflicts of interest

The authors declare that there is no conflict of interests regarding the publication of this article.

Ethical approval

The authors state that this research complies with ethical standards. This research does not involve either human participants or animals.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.School of InformationZhejiang University of Finance and EconomicsHangzhouChina
  2. 2.Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong

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