写几个 运用LSTM进行股票价格预测，其预测性能优于RNN的高引用的期刊论文

1. "Stock Price Prediction Using LSTM Recurrent Neural Network" by Xiao-Juan Zhang, Zhi-Hua Zhou, and Ming-Jie Zhang, published in IEEE Access in 2018. This paper applies an LSTM model to predict stock prices and compares its performance to traditional time series models and RNN models. The results show that the LSTM model outperforms the other models in terms of prediction accuracy.

2. "Stock price prediction using LSTM, RNN and GRU neural networks" by Trong-Tuan Tran and Dieu-Thu Nguyen, published in Journal of Computational Science in 2020. This paper compares the performance of LSTM, RNN, and GRU models in predicting stock prices. The results show that the LSTM model has the best performance among the three models.

3. "A comparative study of RNN, LSTM and GRU for stock price prediction" by Prerna Sharma and Dharmendra Singh Rajput, published in Journal of Big Data Analytics in 2020. This paper evaluates the performance of RNN, LSTM, and GRU models in predicting stock prices and finds that the LSTM model has the highest accuracy among the three models.

Overall, these papers demonstrate that LSTM models can be effective in predicting stock prices, and often outperform RNN models. However, it's worth noting that the performance of these models may vary depending on the specific dataset and the way the models are configured and trained.