Research on Time Series and Multi-Data Prediction Based on BP Neural Network

In recent years, BP neural network has become a powerful tool for time series forecasting due to its excellent nonlinear fitting ability and wide application. In this paper, we propose a parallel processing scheme for BP neural network optimized by combining MapReduce framework and Genetic Algorithm (GA), which aims to solve the problem of fast prediction of large-scale datasets. A dataset parallelization method of BP neural network optimized by GA based on MapReduce, which mainly has two stages: parallelization of GA and parallelization of BP network. GA parallelization adopts a multi-population method for parallelization, and by assigning different initial populations at different nodes, the diversity of populations and individuals can be increased, and the probability of obtaining optimal individuals can be improved. The parallelization map stage of the BP network calculates the change of the weight of each connection in the neural network, which is temporarily stored locally, and finally the Reduce side calculates the average value of the change of ownership, and uses this evaluation change to update the weight, which can speed up the convergence time of the BP network. The results show that in the cluster environment, the convergence speed is significantly accelerated, which has obvious speed advantages compared with traditional single-node training.