摘要：To analyse the characteristics of AIDS transmission from incidence, we used ARIMA and BP neural networks to model the incidence of AIDS and predict them based on modelling. When the sequence is a small sample sequence and instability, the input of the BP neural network can use raw data or stationary sequence in the ARIMA. When using the stationary sequences of incidence as the input of the BP neural network, we can obtain the output corresponding to raw data by matrix operations. Results show that raw data combined with the stationary sequences as the input of the BP neural network can get better modelling results. Moreover, all the predicted values fall within the 95% CI of the ARIMA model. Although there was also a study (reference 14) using BP to predict the incidence of AIDS, it is the original used stationary series as the input of BP in this study.
