The SAS procedure of ARIMA model and its application in time series
Huang Yan · Jiguang zazhi · 2007
Objective:To establishment the SAS procedure of ARIMA Model and to investigate the application of ARIMA predictive model in seasonal time series.Methods:The Parameters of model were got based on conditional least squares.The primitive series may become steady by logarithmic transformation and finite difference.The structure is determined according to criteria of residual un-correlation and concision.The order of model was confirmed through Akaike Information Criterion and Schwarz Bayesian Criterion.So ARIMA predictive model was fitted.Results:For the data of Hepatitis A,the model of ARIMA(0,1,1)(0,1,1)_(12) was established.In this model the estimation of variance is 0.125003,AIC=46.71429,SBC=50.86936.The white-noise residual was analyzed based on the residual analysis.According to the rich table,it shows that the best ARIMA model is(1-B)(1-B~(12))Z_t=(1-0.84397B)(1-0.6649B~(12))α_t.Conclusion:The model of ARIMA can be used to forecast incidence of hepatitis A.And it has a high prediction precision for short-term time series.