摘要
目的:利用SAS程序实现ARIMA模型,探讨ARIMA预测模型在季节性时间序列资料分析中的应用。方法:采用条件最小二乘方法估计模型参数。通过对数转换及差分方法使原始序列平稳,按照残差不相关原则、简洁原则确定模型结构,依据AIC和SBC准则确定模型阶数,最终建立起ARIMA预测模型。结果:对甲型肝炎月发病率资料建立了乘积ARIMA(O,1,1)(0,1,1)12模型。方差估计值为0.125003,AIC=46.71429,SBC=50.86936,时模型进行白噪声残差分析(p=0.7755),根据拟合优度统计量,表明(1-B)(1-B12)Zt=(1-0.84397B)(1-0.6649B12)αt是适合的。结论:用所建立的ARIMA模型对甲型肝炎月发病率进行分析预测,结果表明ARIMA是一种短期预测精度较高的预测模型。
Objective：To establishment the SAS procedure of ARIMA Model and to investigate the application of ARIMA predictive model in seasonal time series.Metheds：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） Zt = （1 -0.84397B） （ 1 -0.6649B^12）αt. Gondusion：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.
出处
《激光杂志》
CAS
CSCD
北大核心
2007年第1期96-96,共1页
Laser Journal
