Forecasting stock price based on fuzzy time-series with entropy-based discretization partitioning

Authors:
Bo-Tsuen Chen;Mu-Yen Chen;Hsiu-Sen Chiang;Chia-Chen Chen
Affiliations:
Department of Information Management, National Taichung Institute of Technology, Taichung, Taiwan, R.O.C;Department of Information Management, National Taichung Institute of Technology, Taichung, Taiwan, R.O.C;Department of Information Management, National Taichung Institute of Technology, Taichung, Taiwan, R.O.C;Department of Information Management, Tunghai University, Taichung, Taiwan, R.O.C
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Year:
2011

Citing 6
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Abstract

The prediction of stock markets is an important and widely research issue since it could be had significant benefits and impacts. In this paper, we applied entropy-based discretization partitioning to obtain optimized linguistic intervals setting for fuzzy time-series model. In order to evaluate our proposed approach, the dataset collected from Taiwan Stock Exchange (TAIEX). Finally, the experimental results showed that our proposed approach was effective in finding for the better linguistic intervals settings, when the entropy-based discretization partitioning is applied. Furthermore, the performances indicate that the proposed model is superior to the compared models suggested by Chen (1996) and Yu (2005) earlier. It is evident that the entropy partitioning is a good approach to obtain optimized linguistic intervals for fuzzy time-series models.