Abstract
In recent years, neural network technology has been widely used in the field of stock forecasting, especially in the aspect of LSTM, which has become a research hotspot because of its unique advantages in dealing with time series problems in stock forecasting. By analyzing and summarizing the recent research literature based on LSTM and its extended model, this paper aims to deeply explore the effect and potential limitations of LSTM in capturing the dynamic and nonlinear characteristics of the stock market. By constructing different combination models combining LSTM and CNN, the paper uses normalization to preprocess the split data and realize the generalization prediction of stock price. Compared with the experimental results, a suitable combination model of LSTM and CNN is found. This hybrid model can significantly improve the accuracy and stability of prediction, and also shows the advantages in multi-input data processing. The results of this study not only provide a new technical means for the direction of stock prediction, but also explore a new direction for the further application of deep learning technology in the financial field.
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