TAIEX forecasting based on fuzzy time series and the automatically generated weights of defuzzified forecasted fuzzy variations of multiple-factors

Authors:
Shyi-Ming Chen;Huai-Ping Chu
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R. O. C.;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R. O. C.
Venue:
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Year:
2010

Citing 8
Cited 0

Fuzzy time series and its models

Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I

Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series

Fuzzy Sets and Systems
A bivariate fuzzy time series model to forecast the TAIEX

Expert Systems with Applications: An International Journal
A new method to forecast the TAIEX based on fuzzy time series

SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Temperature prediction using fuzzy time series

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multivariate Heuristic Model for Fuzzy Time-Series Forecasting

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Handling forecasting problems based on two-factors high-order fuzzy time series

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a new method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series and the automatic generated weights of defuzzified forecasted fuzzy variations of multiple-factors. The proposed method uses the variation magnitude of adjacent historical data to generate fuzzy variation groups of the main factor (i.e., the TAIEX) and the elementary secondary factors (i.e., the Dow Jones, the NASDAQ and the M1B), respectively. Based on the variation magnitudes of the main factor TAIEX and the elementary secondary factors of a particular trading day, it gets the forecasted variation of the TAIEX of the next trading day forecasted by each factor. Based on the correlation coefficients between the forecasted fuzzy variation of the main factor and the forecasted fuzzy variation of each elementary secondary factor, it automatically generates the weights of the defuzzified forecasted fuzzy variation of the main factor and the defuzzified forecasted fuzzy variation of each elementary secondary factor, respectively. Based on the closing index of the TAIEX of the trading day and the weighted forecasted variation, it generates the final forecasted value of the next trading day. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.