Abstract: Conventional forecasting methods cannot systematically analyze the aggregation of space and time in multidimensional parameter analysis. To solve the problem, a prediction method based on discrete process neural networks is proposed in this paper. In order to avoid choosing a local optimal solution during the training of the net, the chaotic particle swarm optimization algorithm is introduced in the process of training. Finally, a case study is presented to illustrate the validity of the proposed method.