Real-Time Forecasting of Stock Trends using Particle Swarm Optimization

The traditional neural network algorithm for stock price forecasting is prone to local optima. To enhance the accuracy of stock price forecasting and reduce forecasting time, this paper introduces an improved Particle Optimisation Neural Network Algorithm. By integrating neural networks and particle swarm optimisation algorithms, a more effective forecasting model is constructed that better reflects the dynamic changes in stock prices. Meanwhile, introducing chaos-interference and mutation factors can enhance the algorithms diversity, thereby further improving forecast accuracy and stability. This method presents a novel solution for research and application in stock price forecasting, offering a valuable reference for relevant practitioners.