LSTM Stock Prediction Model Based on Improved Particle Swarm Optimization

Since LSTM network can effectively deal with time series, it has been widely used to analyze stock prices and future trends. However, the important parameters in the LSTM network are usually determined by experience, which is highly subjective, or the optimal value cannot be determined due to the influence of calculation cost, resulting in the reduction of the fitting ability of the model. Aiming at the above problems, this paper proposes an improved particle swarm algorithm to optimize the key parameters of LSTM network. By reducing the influence of human factors and optimizing the forecasting process, a stock price forecasting model with higher forecasting accuracy is constructed. By utilizing a dynamic multi-swarm particle swarm optimizer to avoid local optimization, the obtained model can improve the optimization performance of PSO algorithm. At the same time, aiming at the problem of high dimension, large noise and data redundancy in the stock market data, which lead to the increase of model training cost and the decrease of prediction performance, a feature selection model is further constructed based on a variety of feature selection algorithms to complete the filtering and screening of index features and build a perfect prediction index system. Finally, it is shown via experimental results that the proposed model can attain higher prediction accuracy and universal applicability in stock price prediction.