基于深度学习的股票趋势预测算法

针对股票趋势预测难的问题，提出一种利用CNN和LSTM进行特征提取，并结合注意力机制和对抗训练的股票趋势预测算法——AACL（Adversarial Attentive CNN-LSTM）算法。该算法利用CNN提取股票的整体趋势信息，LSTM提取股票的短期波动信息，并通过注意力机制将多个股票联系起来，捕捉股票之间的涨跌关系。算法还引入了对抗训练，通过对数据进行干扰，提高算法的鲁棒性。为了验证算法的有效性，在KDD17、ACL18和China50这3个数据集上进行实验，并与现有的算法进行比较，实验结果表明本文提出的算法可以获得最优的预测效果。

Stock Movement Prediction Algorithm Based on Deep Learning

To improve the accuracy of stock movement prediction， this paper proposes a stock movement prediction algorithm AACL（Adversarial Attentive CNN-LSTM）which utilizes CNN and LSTM for feature extraction and combines attention mechanism and adversarial training. The algorithm uses CNN to extract the overall trend information of the stock， LSTM to extract the short-term fluctuation information of the stock， and connects multiple stocks through the attention mechanism to capture the rising and falling relationship between stocks. The algorithm also introduces adversarial training to improve the robustness of the algorithm by interfering the data. To verify the effectiveness of the AACL algorithm， experiments are carried out on three data sets KDD17， ACL18， and China50， and compared with existing algorithms. Experiments results show that the algorithm proposed in this paper can obtain the best result.