Stock price forecasting using secondary self-regression model and wavelet neural networks
Chi-I Yang, Kai-Cheng Wang, Kuei-Fang Chang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
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.