Prediction of the incidence of hepatitis A in Tianjin using the autoregressive integrated moving average model

Objective　The study established a predictive model of multiple seasonal autoregressive integrated moving average (ARIMA) (p, d, q)(P, D, Q)s based on the hepatitis A data, and evaluated its predictive effects. Methods　The primitive series stabilized using the finite difference method, the order of model confirmed according to the Akaike Information Criterion and Schwarz Bayesian Criterion, and the parameters of model obtained through conditional least squares, the ARIMA predictive model was established. Results　The error of the multiple ARIMA(2, 1, 1)(0, 1,1)12 model for the prediction of hepatitis A was 3.72%. Conclusion　The ARIMA was a highly accurate model for short-term prediction of incidence of hepatitis A.