关键词: 股票趋势预测, 卷积自注意力, 去趋势互相关系数

Abstract: With the continuous development of China??s stock market, the trend of a stock is often affected by the development of the upstream and downstream industries of its enterprises. In view of the fact that the mainstream stock prediction model ignores the shortcomings of the correlation relationship between stocks, a stock trend prediction model  fusing graph convolution and long convolution self-attention is proposed. Firstly, the relationship matrix of multiple associated stocks is calculated using the correlation coefficient, then the graph convolutional network combining relationship matrix is used to extract the feature of the associated stocks. Secondly, the multi-head convolution is used to extract long-term features from attention. Finally, the classification loss function polynomial expansion framework is used to make trend prediction for loss function optimization. Experimental results show that the proposed model is superior to gated loop unit, time convolutional network and other models in terms of accuracy, precision, recall and F1 score.

Key words: stock trend forecasting, convolution self-attention, detrend the number of interrelationships