Earticle

초록

영어

Many traditional time series model has been widely applied in forecasting Problem. However, the previous time series methods still have some constraints: (1) conventional time series models only considered single variable; (2) traditional fuzzy time series model determined the interval length of linguistic value subjectively; (3) selecting variables depended on personal experience and opinion. Hence, this paper proposes a novel hybrid fuzzy time series model based on fitting function to forecast TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock index). The proposed model employed Pearson’s correlation to select important technical indicators objectively, and the proposed model utilized fitting function to forecast TAIEX Index. In verification, the collected TAIEX datasets from 1998/01/03 to 2002/12/31 are used as experimental dataset and the root mean square error (RMSE) as evaluation criterion. The results show that the proposed model outperforms the listing models in accuracy.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. Fuzzy Set
  2.2. Fuzzy Time Series
  2.3. Technical Analysis
 3. Proposed Method
 4. Experimental and Results
 5. Conclusions
 References

키워드

저자

• Chung-Ho Su [ Department of Animation and Game Design, Shu-Te University ]
• Ching-Hsue Cheng [ Department of Information Management, National Yunlin University of Science and Technology ]
• Wei-Lun Tsai [ Department of Information Management, National Yunlin University of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605