NDLI: A neuro-fuzzy based method for TAIEX forecasting

Content Provider	IEEE Xplore Digital Library
Author	Zhao-Yu Wang Shie-Jue Lee
Copyright Year	2014
Description	Author affiliation: Dept. of Electr. Eng., Nat. Sun Yat-Sen Univ., Kaohsiung, Taiwan (Zhao-Yu Wang; Shie-Jue Lee)
Abstract	Time series prediction can be widely applied to a variety of fields. Recently, a lot of artificial intelligence (Al) techniques have been exploited in the task of time series prediction. Compared to statistical methods, Al techniques are easier to use for real world data, and their performance can be better. In this paper, we propose a neuro-fuzzy based system for time series prediction. The neuro-fuzzy based system can generate superior performance through the relationship among different features. By partitioning the training data into clusters, fuzzy IF-THEN rules are extracted to form a fuzzy rule-base. Then, a fuzzy network is constructed accordingly and parameters are refined to increase the precision of the fuzzy rule-base by applying a hybrid learning algorithm which combines a recursive singular value decomposition-based least squares estimator and the gradient descent method. We demonstrate the effectiveness of the proposed system by applying it to do prediction for TAIEX stock indices. The experimental results conclude the superiority of the proposed system over other existing systems.
Starting Page	579
Ending Page	584
File Size	1236670
Page Count	6
File Format	PDF
ISBN	9781479942169
ISSN	2160133X
e-ISBN	9781479942152
DOI	10.1109/ICMLC.2014.7009672
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Abstracts Neural networks learning algorithm neuro-fuzzy based system time series fuzzy rule base
Content Type	Text
Resource Type	Article

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