scholarly journals Network structure optimization algorithm for information propagation considering edge clustering and diffusion characteristics

2018 ◽  
Vol 67 (19) ◽  
pp. 190502
Author(s):  
Yang Li ◽  
Song Yu-Rong ◽  
Li Yin-Wei
Keyword(s):  
Network Structure ◽  
Optimization Algorithm ◽  
Structure Optimization ◽  
Information Propagation ◽  
Diffusion Characteristics ◽  
Network Structure Optimization ◽  
And Diffusion

scholarly journals Comparative Research on Influencing Factors of LSTM Deep Neural Network in Stock Market Time Series Prediction

2019 ◽  
Vol 4 (1) ◽  
pp. 84
Author(s):  
TANG Yin ◽  
YANG Jin Yu ◽  
CHEN Jian
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Influencing Factors ◽  
Network Structure ◽  
Time Series Prediction ◽  
Structure Optimization ◽  
Optimization Techniques ◽  
Training Process ◽  
Bear Market ◽  
Network Structure Optimization

<p><em>During training process of LSTM, the prediction accuracy is affected by a variation of factors, including the selection of training samples, the network structure, the optimization algorithm, and the stock market status. This paper tries to conduct a systematic research on several influencing factors of LSTM training in context of time series prediction. The experiment uses Shanghai and Shenzhen 300 constituent stocks from 2006 to 2017 as samples. The influencing factors of the study include indicator sampling, sample length, network structure, optimization method, and data of the bull and bear market, and this experiment compared the effects of PCA, dropout, and L2 regularization on predict accuracy and efficiency. Indice sampling, number of samples, network structure, optimization techniques, and PCA are found to be have their scope of application. Further, dropout and L2 regularization are found positive to improve the accuracy. The experiments cover most of the factors, however have to be compared by data overseas. This paper is of significance for feature and parameter selection in LSTM training process.</em></p>

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