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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
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.
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.
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