SAS ARIMA 模型

data ex1;
input price @@;
time=intnx('week', '13OCT2006'd, _n_-1);
format time date7.;
cards;
10.3000 8.5269 9.0421 10.1727 9.9079 8.9714 9.0145 9.4738 9.5258 9.7017
10.0582 9.5292 8.9786 9.1743 9.8478 9.6218 9.0342 9.1891 9.6062 9.8946
9.4853 9.2557 9.2805 9.5258 10.1192 9.6384 8.8495 9.2644 9.4939 9.6623
9.4212 9.5570 9.7627 9.5639 8.7962 9.1777 10.2288 10.3722 9.0861 8.8148
9.2055 9.4473 9.2903 9.5358 9.5294 9.5368 9.4168 9.3237 9.5939 9.8874
10.3007 9.3051 8.6804 9.5337 9.8757 9.2799 9.3030 10.0135 10.1025 10.1310
9.6605 9.8175 9.4935 9.0052 9.2178 10.0131 9.6019 9.4843 9.2807 9.4567
;

proc gplot;
plot price*time/ vaxis=8.5 to 10.5 by 0.1;
symbol c=red i=join v=star;
run;

proc arima data=ex1;
identify var=price stationarity=(adf) nlag=10;
run;

proc arima data=ex1;
identify var=price minic p=(0:6) q=(0:6);
run;

proc arima data=ex1;
identify var=price;
estimate p=2 method=ml;
forecast lead=5 id=time interval=week out=ex2;
run;

proc gplot data=ex2;
plot price*time=1 forecast*time=2 l95*time=3 u95*time=3/overlay;
symbol1 c=black i=join v=diamond;
symbol2 c=red i=join v=dot;
symbol3 c=green i=join v=circle;
run;