Research on Time Series Prediction Based on Genetic Algorithm Optimized BP Neural Network

Time series prediction has significant applications in areas such as manufacturing process monitoring and energy consumption forecasting. However, traditional BP neural networks have limitations in handling complex time series data. This paper investigates a method of optimizing BP neural networks using genetic algorithms (GA). By leveraging GA to search for global optimal parameters, the prediction performance of BP neural networks is enhanced. In the study, time series datasets were utilized for experiments, and the results demonstrated that GA-optimized BP neural networks significantly improved prediction accuracy and stability. This research aims to address the shortcomings of traditional time series prediction methods and provide a new solution for achieving higher prediction accuracy. The findings have important theoretical significance and broad application value. These research findings provide strong support for fields such as intelligent manufacturing and industrial automation, offering a valuable reference for future research in related areas.