关键词: 长短期记忆网络（LSTM）, 卷积神经网络（CNN）, 注意力机制, 股价预测

Abstract:

In order to better predict stock prices and provide reasonable suggestions for stockholders, a hybrid stock prediction model（LSTM-CNN-CBAM） that incorporates attention mechanism based on Long Short and Term Memory（LSTM） network and Convolutional Neural Network（CNN） is proposed. The model uses an end-to-end network structure. LSTM is used to extract the time-series features in the data, and then CNN is used to mine the deep features in the data. By adding an attention mechanism to the network structure Convolutional Attention Block Module convolution module, which can effectively improve the feature extraction capability of the network. Based on the Shanghai Stock Exchange Index, a comparative experiment is performed. By comparing the experimental prediction results and evaluation indicators, the prediction effectiveness and feasibility of adding the CBAM module to the network model combining LSTM and CNN are verified.

Key words: Long Short and Term Memory（LSTM） network, Convolutional Neural Network（CNN）;attention mechanism, stock price forecasting