Financial data prediction based on LSTM model

In order to improve the effectiveness and accuracy of financial data forecasting, this paper first reviewed the traditional methods of financial data forecasting and the prediction models based on machine learning in recent years through literature review, and pointed out the potential advantages of LSTM model in solving the problem of financial time series forecasting. Then the steps of data collection and preprocessing are elaborated, the LSTM prediction model is constructed, and the model architecture, parameter setting and training process are explained in detail. In order to verify the validity and accuracy of the model, an experiment was designed to analyze the performance difference between the LSTM model and other prediction models through comparative experiments. The experimental results show that the LSTM model has high accuracy and stability in financial data prediction, especially in the processing of complex time series data. The research results of this paper not only provide a new and effective method for financial data forecasting, but also provide a useful reference for subsequent relevant research.