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Date:         Tue, 19 Oct 2010 00:26:00 +0100
Reply-To:     "adel F." <adel_tangi@YAHOO.FR>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         "adel F." <adel_tangi@YAHOO.FR>
Subject:      proc arima
Content-Type: text/plain; charset=utf-8

Hi all, I would be very thankful if you could comment on my pgm below. I have followed example 7.2 (International airline passengers) in chapter 7 (the arima procedure). My problem is the following , I have a dataset on people being recruited for a trial from January 08 to August 10. We aim to recruit 4500. My objective by using proc arima is to predict the time I will need to acheive the target of 4500 by July 2012. I have used time series analysis, I would be thankful if you could advise whether the approach I have taken is correct or not, or whether the model I have considered is correct or not. Below is the dataset and the modelling Thanks a lot Adel data mydataset; retain time 0; input date:monyy5. numpeople @@; time = time + 1; lag_people = LAG(numpeople); xlog=log(numpeople); format date monyy5.; datalines; Jan08 38 Feb08 33 Mar08 38 Apr08 37 May08 49 Jun08 48 Jul08 66 Aug08 47 Sep08 70 Oct08 66 Nov08 72 Dec08 41 Jan09 75 Feb09 89 Mar09 82 Apr09 70 May09 55 Jun09 76 Jul09 79 Aug09 49 Sep09 82 Oct09 85 Nov09 81 Dec09 64 Jan10 63 Feb10 98 Mar10 95 Apr10 75 May10 89 Jun10 96 Jul10 103 Aug10 69 ; run; symbol i=join v=dot; proc gplot data=mydataset; plot numpeople* date =1; run; proc arima data=mydataset; identify var=xlog(1,12) nlag=15; estimate q=(1)(12) noconstant method=uls; run; forecast out=b lead=24 id=date interval=month noprint; run; data c; set b; x=exp(xlog); forecast=exp(forecast +std*std/2); l95=exp(l95); u95=exp(u95); run; symbol1 i=none v=star; symbol2 i=join v=circle; symbol3 i=join v=none l=3; proc gplot data=c; where date >= '1JAN08'd; plot x*date = 1 forecast * date = 2 l95 * date = 3 u95 * date = 3 / overlay haxis= '1JAN08'd to '1AUG12'd by year; run;


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