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Journal of Applied Sciences
  Year: 2013 | Volume: 13 | Issue: 16 | Page No.: 3335-3339
DOI: 10.3923/jas.2013.3335.3339
 
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A Fuzzy Time Series Model Based on Genetic Discretization Approach

Jing-Rong Chang and Chung-Chi Liu

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.
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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

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