Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (3): 203-207.

### Research on Stock Forecasting Based on LSTM-CNN-CBAM Model

ZHAO Hongrui, XUE Lei

1. College of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
• Online:2021-02-01 Published:2021-01-29

### 基于LSTM-CNN-CBAM模型的股票预测研究

1. 上海大学 通信与信息工程学院，上海 200444

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.