Application Of Particle Swarm Optimization and Improved PSO-BP Algorithm in Computer Forecasting Model

Abstract

Given the shortcomings of the traditional BP (Back Propagation) neural network in dealing with medium and long-term stock forecasting, this paper presents a combination forecasting model of BP network optimized by PSO (Particle Swarm Optimization). Firstly, the ARIMA (Autoregressive Integrated Moving Average) model was used to predict the stock's closing price, which was used as part of the BP neural network input. On this basis, the BP network was optimized by particle swarm optimization algorithm, and the optimal threshold and weight were given to the network for further prediction, which improved the generalization ability and prediction accuracy of the whole network. The results show that, compared with BP neural network, LSTM (Long Short-Term Memory) and ARIMA model, the PSO-BP network model has a significant improvement in forecasting accuracy, which verifies the effectiveness of the combination forecasting method in dealing with the problem of medium and long-term stock price forecasting.