Application of type-2 neuro-fuzzy modeling in stock price prediction

被引：62
|
作者：
Liu, Chih-Feng [1 ]
Yeh, Chi-Yuan [1 ]
Lee, Shie-Jue [1 ]
机构：
[1] Natl Sun Yat Sen Univ, Dept Elect Engn, Kaohsiung 804, Taiwan
来源：
APPLIED SOFT COMPUTING | 2012年 / 12卷 / 04期
关键词：
Stock forecasting; Type-2 fuzzy set; TSK rule; Self-constructing fuzzy clustering; Particle swarm optimization; Least squares estimation; TIME-SERIES PREDICTION; GENETIC ALGORITHMS; SYSTEM; NETWORKS; FORECAST;
D O I：
10.1016/j.asoc.2011.11.028
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present an application of type-2 neuro-fuzzy modeling to stock price prediction based on a given set of training data. Type-2 fuzzy rules can be generated automatically by a self-constructing clustering method and the obtained type-2 fuzzy rules cab be refined by a hybrid learning algorithm. The given training data set is partitioned into clusters through input-similarity and output-similarity tests, and a type-2 TSK rule is derived from each cluster to form a fuzzy rule base. Then the antecedent and consequent parameters associated with the rules are refined by particle swarm optimization and least squares estimation. Experimental results, obtained by running on several datasets taken from TAIEX and NASDAQ, demonstrate the effectiveness of the type-2 neuro-fuzzy modeling approach in stock price prediction. Crown Copyright (c) 2011 Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：1348 / 1358
页数：11
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