Financial time series data forecasting by Wavelet and TSK fuzzy rule based system

被引：1
|
作者：
Chang, Pei-Chann [1 ,2 ]
Fan, Chin-Yuan [2 ]
Chen, Shih-Hsin [2 ]
机构：
[1] Yuan Ze Univ, Dept Informat Management, Taoyuan 32026, Taiwan
[2] Yuan Ze Univ, Dept Ind Engn & Managemenet, Taoyuan 32026, Taiwan
来源：
FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 3, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/FSKD.2007.290
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this study, a novel approach by integrating the wavelet and Takagi-Sugeno-Kang (TSK) fuzzy rule based systems (FRBS) for financial time series data prediction is developed. The wavelet method is applied to eliminate the noises caused by random fluctuations. The data output from the wavelet is then input to the TSK fuzzy rule system for prediction of the future value of a time series data. Through the intensive experimental tests, the model has successfully forecasted the price variation for stocks with accuracy close to 97.6% in TSE index.
引用
收藏
页码：331 / +
页数：2
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