@article{He2022, 
author = {Hongzeng He and Shufen Dai},
title = {A prediction model for stock market based on the integration of independent component analysis and Multi-LSTM},
year = {2022},
journal = {Electronic Research Archive},
volume = {30},
number = {10},
pages = {3855-3871},
keywords = {independent component analysis, Multi-LSTM, stock market, complex network, prediction model},
url = {https://www.sciopen.com/article/10.3934/era.2022196},
doi = {10.3934/era.2022196},
abstract = {In this paper, we investigate the statistical behaviors of the stock market complex network. A hybrid model is proposed to predict the variations of five stock prices in the securities plate sub-network. This model integrates independent component analysis (ICA) and multivariate long short-term memory (Multi-LSTM) neural network to analyze the trading noise and improve the prediction accuracy of stock prices in the sub-network. Firstly, we apply ICA to deconstruct the original dataset and remove the independent components that represent the trading noise. Secondly, the rest of the independent components are given to Multi-LSTM neural network. Finally, prediction results are reconstructed from the outputs of the Multi-LSTM neural network and the corresponding mixing matrix. The experiment results indicate that the hybrid model outperforms the benchmark approaches, especially in terms of the stock market complex network.}
}