Dynamic gaussian deep belief network design and stock market application

Abstract

Stock price forecasting has been an important topic for investors, researchers, and analysts. In this paper, a prediction model of Dynamic Gaussian Deep Belief Network (DGDBN) is proposed. Generally, the network structure of traditional Deep Belief Network (DBN) determines the performance of its time series prediction. Most previous research uses artificial experience to adjust the network structure, it is difficult to ensure performance and time efficiency by constantly trying. In addition, the accuracy of the traditional DBN stacked by binary Restricted Boltzmann Machines(RBM) needs to be improved when solving the time series problem. The DGDBN designed in this paper contains two points: The first point is to add Gaussian noise to the RBM. The second point is to realize the increase or decrease branch algorithm of hidden layer structure according to the connection weights and average percentage error (MAPE). Finally, the forecast for the stocks of United Technologies Corporation and Unisys Corp, DGDBN is compared with DBN and LSTM. The root means square error (RMSE) increases by 15% and 65%. The interesting thing we found is that the number of neurons in the last layer of the DGDBN network has a greater effect than other layers.