data arima.js_dispos; set arima.js_2005to2018; label year="y" cases="c"; run; proc gplot data=arima.js_dispos; plot cases*year; symbol i=spline v=star ci=red cv=bib; run; proc arima data=arima.js_dispos; identify var=cases stationarity=(adf=1); run; data arima.js_dispos_dif2; set arima.js_dispos; z=cases; difz=dif(z); difz2=dif(difz); label z="n" difz="d1" difz2="d2" ; run; proc gplot data=arima.js_dispos_dif2; plot (z difz difz2)*year; symbol i=spline v=star ci=red cv=bib; run; proc arima data=arima.js_dispos_dif2; identify var=difz2 stationarity=(adf=1); run; proc arima data=arima.js_dispos_dif2; identify var=difz2 stationarity=(adf=1) WHITENOISE=IGNOREMISS; run; proc arima data=arima.js_dispos_dif2; identify var=difz2 minic perror=(8:11); run; /*test proc arima data=arima.js_dispos_dif2; identify var=difz2 minic P=(0:5) Q=(0:5) ; run; ---------------test end*/ proc arima data=arima.js_dispos_dif2; identify var=z(2) nlag=6; estimate q=0 p=0 method=ml; forecast lead=5 out=arima.outvalue id=year; run; quit;
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