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• W44081553 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." @default.
• W44081553 created "2016-06-24" @default.
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• W44081553 date "2011-01-01" @default.
• W44081553 modified "2023-09-23" @default.
• W44081553 title "Forecasting Stock Price Based on Fuzzy Time-Series with Entropy-Based Discretization Partitioning" @default.
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• W44081553 doi "https://doi.org/10.1007/978-3-642-23863-5_39" @default.
• W44081553 hasPublicationYear "2011" @default.
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