Integrating Harmony Search Algorithm and Deep Belief Network for Stock Price Prediction Model

2019 ◽  
Author(s):  
Lei Zhang ◽  
Xiangqian Ding ◽  
Ruichun Hou ◽  
Ye Tao
Keyword(s):  
Prediction Model ◽  
Stock Price ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Deep Belief Network ◽  
Belief Network ◽  
Stock Price Prediction ◽  
Price Prediction

Hybrid ARIMA-deep belief network model using PSO for stock price prediction

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Biju R. Mohan ◽  
Shaikh Sahil Ahmed ◽  
Mahesh Kankar ◽  
Nagaraj Naik
Keyword(s):  
Network Model ◽  
Stock Price ◽  
Deep Belief Network ◽  
Belief Network ◽  
Stock Price Prediction ◽  
Price Prediction

Stock Price Prediction Model Based on IWO Neural Network and its Applications

2014 ◽  
Vol 989-994 ◽  
pp. 1635-1640
Author(s):  
Hong Liu ◽  
Xiao Yan Lv
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Steepest Descent Method ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based

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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