Jing-Rong Chang
Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan
Chung-Chi Liu
Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan
ABSTRACT
There are many uncertainty problems in the Human society, such as the forecasting of economic growth rate, financial crisis, etc. Since Song and Chissom (1993) proposed the concept of fuzzy time series, many scholars have proposed different models to deal with these problems. However, previous studies usually do not consider the transfer original data to the fuzzy linguistic value by the subjective opinions in fuzzy process, which cannot objectively show the characteristics of the data. Based on above concepts, the purpose of this study is to explore ways of determining the objective lengths of intervals in fuzzy time series. This study proposed a high-order weighted fuzzy time series model based on Genetic Discretization Approach (GDA). In order to verify the proposed method, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) from the (http://www.twse.com.tw) are used in the experiment and the experiment results are compared with other methods in with this study. The forecasting performance shows that the proposed method having better forecasting ability.
PDF References Citation
How to cite this article
Jing-Rong Chang and Chung-Chi Liu, 2013. A Fuzzy Time Series Model Based on Genetic Discretization Approach. Journal of Applied Sciences, 13: 3335-3339.
DOI: 10.3923/jas.2013.3335.3339
URL: https://scialert.net/abstract/?doi=jas.2013.3335.3339
DOI: 10.3923/jas.2013.3335.3339
URL: https://scialert.net/abstract/?doi=jas.2013.3335.3339
REFERENCES
- Song, Q. and B.S. Chissom, 1993. Forecasting enrollments with fuzzy time series-Part I. Fuzzy Sets Syst., 54: 1-9.
CrossRefDirect Link - Chen, S.M. and N.Y. Chung, 2006. Forecasting enrollments using high-order fuzzy time series and genetic algorithms. Int. J. Intell. Syst., 21: 485-501.
CrossRefDirect Link - Chang, J.R., Y.T. Lee, S.Y. Liao and C.H. Cheng, 2007. Cardinality-based fuzzy time series for forecasting enrollments. Proceedings of the 20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, June 26-29, 2007, Kyoto, Japan, pp: 735-744.
CrossRefDirect Link - Chen, S.M. and K. Tanuwijaya, 2011. Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques. Expert Syst. Appl., 38: 10594-10605.
CrossRefDirect Link - Chen, S.M.and C.D. Chen, 2011. TAIEX forecasting based on fuzzy time series and fuzzy variation groups. IEEE Trans. Fuzzy Syst., 19: 1-12.
CrossRefDirect Link - Wei, L.Y., 2012. An adaptive expectation genetic algorithm based on anfis and multinational stock market volatility causality for TAIEX forecasting. Cybern. Syst. Int. J., 43: 410-425.
CrossRefDirect Link - Cheng, C.H., L.Y. Wei, J.W. Liu and T.L. Chen, 2013. OWA-based ANFIS model for TAIEX forecasting. Econ. Modell., 30: 442-448.
CrossRefDirect Link - Su, C.H., C.H. Cheng and W.L. Tsai, 2013. Fuzzy time series model based on fitting function for forecasting TAIEX index. Int. J. Hybrid Inform. Technol., 6: 111-122.
Direct Link