TAIEX Forecasting Based on Fuzzy Time Series and Fuzzy Variation Groups

Shyi‐Ming Chen · Chao-Dian Chen

177 citations0/0 files storedIEEE Transactions on Fuzzy Systems

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Stock Market Forecasting Methods Forecasting Techniques and Applications Fuzzy Logic and Control Systems

Abstract

In this paper, we present a new method to forecast the daily Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series and fuzzy variation groups, where the main input factor is the previous day's TAIEX, and the secondary factor is either the Dow Jones, the NASDAQ, the M1b, or their combination. First, the proposed method fuzzifies the historical training data of the TAIEX into fuzzy sets to form fuzzy logical relationships. Second, it groups the fuzzy logical relationships into fuzzy logical relationship groups (FLRGs) based on the fuzzy variations of the secondary factor. Third, it evaluates the leverage of the fuzzy variations between the main factor and the secondary factor to construct fuzzy variation groups. Fourth, it gets the statistics of the fuzzy variations appearing in each fuzzy variation group. Fifth, it calculates the weights of the statistics of the fuzzy variations appearing in each fuzzy variation group, respectively. Finally, based on the weights of the statistics of the fuzzy variations appearing in the fuzzy variation groups and the FLRGs, it performs the forecasting of the daily TAIEX. Because the proposed method uses both fuzzy variation groups and FLRGs to analyze in detail the historical training data, it gets higher forecasting accuracy rates to forecast the TAIEX than the existing methods.

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References

1965Fuzzy sets 1996Fuzzy sets 1993Forecasting enrollments with fuzzy time series — Part I 1996Forecasting enrollments based on fuzzy time series 1994Forecasting enrollments with fuzzy time series — part II 2001Effective lengths of intervals to improve forecasting in fuzzy time series 2002FORECASTING ENROLLMENTS BASED ON HIGH-ORDER FUZZY TIME SERIES 2004Weighted fuzzy time series models for TAIEX forecasting

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2014A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression 2013TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines 2014Fuzzy Forecasting Based on Two-Factors Second-Order Fuzzy-Trend Logical Relationship Groups and the Probabilities of Trends of Fuzzy Logical Relationships 2012Fuzzy Forecasting Based on Two-Factors Second-Order Fuzzy-Trend Logical Relationship Groups and Particle Swarm Optimization Techniques 2016Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors

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