摘要
Abstract
A hybrid deep learning stock index futures price prediction model based on VAE-ATTGRU is proposed,using variational autoencoder(VAE)and the recurrent neural network(RNN),to address the difficulty of predicting high-volatility,non-stationary,non-linear,and high signal-to-noise ratio characteristics in the stock index futures market.Firstly,the tech-nical indicators of the stock index futures are learned using the VAE,and the latent factors learned by VAE are fused with the original data to achieve data augmentation and obtain a richer factor representation.Secondly,RNN is used to predict the stock index futures prices.It is found that the gated recurrent unit(GRU)combined with the attention mechanism(ATTGRU)can fully learn from the stock index futures data enhanced by VAE,capture key feature information,and reas-sign weights.The VAE-ATTGRU model is evaluated on datasets such as the CSI300 stock index futures,the CSI 500 index futures,and the SSE 50 index futures using root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination R2.The experimental results demonstrate that the VAE-ATTGRU model outperforms other models in terms of prediction accuracy.关键词
股指期货预测/变分自编码器(VAE)/数据增强/注意力机制/门控循环单元(GRU)Key words
stock index futures prediction/variational autoencoder(VAE)/data augmentation/attention mechanism/gated recurrent unit(GRU)分类
信息技术与安全科学
