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Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm

Shyi-Ming Chen1, Gandhi Maruli Tua Manalu, Jeng-Shyang Pan

  • 1Department of Computer Science and Information Engineering, National Taiwan University of Scienceand Technology, Taipei 106, Taiwan. smchen@mail.ntust.edu.tw

IEEE Transactions on Cybernetics
|November 30, 2012
PubMed
Summary

This study introduces a novel fuzzy forecasting method using fuzzy-trend logical relationships and particle swarm optimization (PSO). The approach enhances predictive accuracy for financial time series like stock indices and exchange rates.

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Area of Science:

  • Computational intelligence
  • Financial forecasting
  • Time series analysis

Background:

  • Accurate financial forecasting is crucial for economic stability.
  • Existing fuzzy forecasting methods have limitations in capturing complex relationships.
  • Particle swarm optimization (PSO) is a powerful optimization technique.

Purpose of the Study:

  • To develop a novel fuzzy forecasting method.
  • To integrate two-factors second-order fuzzy-trend logical relationships with PSO.
  • To improve the accuracy of financial time series forecasting.

Main Methods:

  • Fuzzification of historical data for main and secondary factors.
  • Formation of two-factors second-order fuzzy logical relationships.
  • Grouping into fuzzy-trend logical relationship groups.
  • Optimization of weighting vectors using PSO for forecasting.

Main Results:

  • The proposed method successfully forecasts the Taiwan Stock Exchange Capitalization Weighted Stock Index.
  • The method accurately predicts NTD/USD exchange rates.
  • Experimental results demonstrate superior forecasting performance compared to existing methods.

Conclusions:

  • The novel fuzzy forecasting method offers improved accuracy.
  • The integration of fuzzy-trend logical relationships and PSO is effective.
  • This approach provides a valuable tool for financial market prediction.