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Date:         Wed, 16 Dec 2009 18:30:08 -0500
Reply-To:     Francois van der Walt <francoisw@GJI.COM.AU>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Francois van der Walt <francoisw@GJI.COM.AU>
Subject:      Re: Trend matching
Comments: To: Sid N <nsid31@GMAIL.COM>

Dear Sid,

I have quickly read your sample data and plotted it using proc gplot. From looking at the graphs of your sample data I can see that analyzing and exploring for seasonality will not help you. I also do not think that trying to combine it with similarly trended other products will give you any benefit either.

If I had to answer this I would look at only the last 3 points to estimate the trend. I clearly do not know the bigger picture like, how many regions or what other considerations their is from the bigger project.

I am more than happy to show you the graphs and share more of my experience working on similar data for many years.

Kind Regards Francois

On Wed, 16 Dec 2009 17:52:59 -0500, Sid N <nsid31@GMAIL.COM> wrote:

>Sigurd, > >Thank you for your reply. Yes, I am looking at processes that have declining >trends. This is one of the approaches that I am taking as part of a larger >forecasting project. As some of the regions do not have sufficient data >points, I am not sure how useful traditional forecasting tools are in >predicting future production. > >My approach in comparing production from different regions and then >predicting production based on trends from matching regions also involves >other similar characteristics that are shared by these regions. In the >original example, based on the characteristics that these regions share, I >could have matched trends from B with C, but A seemed a closer match based >on the cumulative production. > >I will look at the SAS/ETS documentation to see if I could use any of the >procedures included. > >Sid > >On Wed, 16 Dec 2009 17:30:24 -0500, Sigurd Hermansen <HERMANS1@WESTAT.COM> >wrote: > >>Sid: >>Why are you supposing that future numbers in B will have the same trend as >past numbers in A? It would help to know how you might use the results of >your analysis. Are you looking at processes that deplete resources and >thereafter have rapidly declining trends? I recall seeing Box-Jenkins >estimates of cycles in time series and transfer functions that use cycle >parameter estimates to predict cycles in other series. For that see >documentation on-line for the SAS/ETS product. >>S >> >>-----Original Message----- >>From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of Sid N >>Sent: Wednesday, December 16, 2009 5:11 PM >>To: SAS-L@LISTSERV.UGA.EDU >>Subject: Trend matching >> >>Hi, >> >>I am looking for appropriate analysis steps or procedures that will enable >>me to match production trends based on cumulative production. Below dataset >>has historical production reported from multiple regions. >> >>data have; >>input region $ year prod; >>datalines; >>A 2000 100 >>A 2001 200 >>A 2002 250 >>A 2003 275 >>A 2004 450 >>A 2005 500 >>A 2006 300 >>A 2007 150 >>A 2008 75 >>A 2009 25 >>B 2004 125 >>B 2005 200 >>B 2006 250 >>B 2007 300 >>B 2008 425 >>C 2000 150 >>C 2001 450 >>C 2002 500 >>C 2003 550 >>C 2004 900 >>C 2005 1000 >>C 2006 750 >>C 2007 600 >>D 2006 125 >>D 2007 500 >>D 2008 550 >>D 2009 600 >>; >>run; >> >> >>If the production history of a region spans <= 5 years (as in B and D) then >>the production trend is matched with trends from other regions which have >>longer production history (based on cumulative production). From the above >>dataset, B (cumulative production = 1300) is matched with A (1275 over 5 >>years) and D (1775) with C (1650 over 4 years). Assuming the same future >>trend as A, production from B is predicted for the remaining number of >>years. That is, production from region B is predicted for 5 more years. >>Likewise, future production from D is predicted based on production from C. >>In this case, production from D is predicted for 4 more years. >> >>The example above is only a subset of the actual data. Therefore, trend >>matching in my actual dataset may need to be done by comparing data from a >>larger number of regions. I am looking for an efficient way to accomplish >>this. Any suggestions? >> >>Thank you in advance. >> >>Sid


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