Objective To compare and evaluate the effect of different time series models in predicting incidence of healthcare-associated infection (HAI), and explore the best model for predicting incidence of HAI. Methods Seasonal autoregressive integrated moving average (ARIMA) model, nonlinear autoregressive neural network (NARNN), and ARIMA-back propagation neural network (ARIMA-BPNN) combination model were constructed based on fitting dataset of monthly HAI incidence from 2011 to 2016 (72 months) in a tertiary first-class hospital in Shanghai, predicting dataset of monthly infection incidence from January to December 2017 were used to test the predictive effect of model, the predictive effect of different models was evaluated and compared. Results For the fitting dataset, mean absolute percentage error (MAPE) of ARIMA, NARNN, and ARIMA-BPNN combination model were 13.00%, 14.61%, and 11.95% respectively; and for the predicting dataset, MAPE of ARIMA, NARNN, and ARIMA-BPNN combination model were 15.42%, 26.31%, and 14.87% respectively. Conclusion Three time series models can effectively predict the incidence of HAI, of which the ARIMA-BPNN combination model showed the best performance in fitting and predicting the occurrence of HAI in this hospital, and can provide data support for the hospital decision-making.