Paper Info
Title
A neuro-fuzzy based method for TAIEX forecasting
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.
Year
DOI
Venue
2014
10.1109/ICMLC.2014.7009672
ICMLC
Keywords
Field
DocType
ecursive singular value decomposition-based least squares estimator,fuzzy set theory,statistical methods,al techniques,artificial intelligence,fuzzy rule base,gradient descent method,neuro-fuzzy based system,hybrid learning algorithm,taiex stock indices,real world data,stock markets,neuro-fuzzy based method,fuzzy rule-base,taiex forecasting,fuzzy network,gradient methods,data mining,time series prediction,time series,fuzzy neural nets,singular value decomposition,fuzzy if-then rules,learning algorithm,neural networks
Data mining,Neuro-fuzzy,Defuzzification,Fuzzy classification,Computer science,Fuzzy set operations,Fuzzy logic,Artificial intelligence,Adaptive neuro fuzzy inference system,Fuzzy associative matrix,Fuzzy number,Machine learning
Conference
Volume
ISSN
Citations 
2
2160-133X
0
PageRank 
References 
Authors
0.34
8
2
Name
Order
Citations
PageRank
Zhao-Yu Wang100.34
Shie-Jue Lee2485.11