Recurrent neural network with kernel feature extraction for stock prices forecasting

被引：6
|
作者：
Sun, Xiang [1 ]
Ni, Yong [1 ]
机构：
[1] Hefei Univ Technol, Sch Management, Hefei 230009, Peoples R China
来源：
2006 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, PTS 1 AND 2, PROCEEDINGS | 2006年
关键词：
D O I：
10.1109/ICCIAS.2006.294269
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A two-stage neural network architecture constructed by combining recurrent neural network (RNN) with kernel feature extraction is proposed for stock prices forecasting. In the first stage, kernel independent component analysis (KICA) and kernel principal component analysis (KPCA) are used as feature extraction. In the second stage, RNN with kernel feature extraction is used to regression estimation. By examining the stock prices data, it is shown that (1) RNN with feature extraction outperforms single RNN; (2) RAW with kernel performs better than those without kernel.
引用
收藏
页码：903 / 907
页数：5
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