Academic Journal of Computing & Information Science, 2022, 5(12); doi: 10.25236/AJCIS.2022.051215.
Keren He1, Qian Jiang2
1Information Center, Changzhou University, Changzhou, China
2School of Computer and Artificial Intelligence, Changzhou University, Changzhou, China
The traditional resolution to forecast stock trends accurately is based on time series models. However, traditional time series models simply cannot fit the irregular movements of the market due to their own limitations.This study combines the advantages of convolutional neural networks (CNN) and long short-term memory networks (LSTM) for improving the stock prediction accuracy, uses the convolution idea of CNN to construct feature extraction layer to extract features, and inputs the extracted features into LSTM, to study the temporal message of the features better. The model constructed in this study can capture local features and time series information of stock data.The performance indicators of the proposed model are shown to be outperformed by CNN and LSTM, and the method is effective in reducing forecasting errors.
Stock prediction; Convolutional neural networks; Long short-term memory networks; Feature extraction
Keren He, Qian Jiang. Research on stock prediction algorithm based on CNN and LSTM. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 12: 98-106. https://doi.org/10.25236/AJCIS.2022.051215.
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