Design of stock price prediction model with various configuration of input features

Author(s):  
Yoojeong Song ◽  
Jongwoo Lee
2014 ◽  
Vol 989-994 ◽  
pp. 1635-1640
Author(s):  
Hong Liu ◽  
Xiao Yan Lv

In view of the deficiency of the standard back-propagation algorithm based on steepest descent method, a new kind of optimization strategy called invasive weed optimization (IWO) algorithm is introduced into the training process of feed-forward neural networks, and then a prediction model based on IWO feed-forward neural network (IWO-NN) is given. By the dynamic adjustment of standard deviation of the distribution of offspring individuals in IWO, the local convergence speed of networks is improved and the defect of trapping into a local optimum is reduced. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has better global astringency, robustness, and it is insensitive to initial values.


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