Stock Price Forecasting Using Secondary Self-regression Model and Wavelet Neural Networks

被引：0

作者：

Yang, Chi-I ^{[1
]}

Wang, Kai-Cheng ^{[1
]}

Chang, Kuei-Fang

机构：

[1] Feng Chia Univ, PhD Program Mech & Aeronaut Engn, Taichung 40724, Taiwan

来源：

SEVENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2015) | 2015年 / 9631卷

关键词：

Particle swarm optimization; stock forecasting; wavelet neural network; secondary self-regression model;

D O I：

10.1117/12.2196914

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We have established a DWT-based secondary self-regression model (AR(2)) to forecast stock value. This method requires the user to decide upon the trend of the stock prices. We later used WNN to forecast stock prices which does not require the user to decide upon the trend. When comparing these two methods, we could see that AR(2) does not perform as well if there are no trends for the stock prices. On the other hand, WNN would not be influenced by the presence of trends.

引用

页数：8

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