Thank you guys for the responses, they have been helpful ... View more

Hello - All time series based forecasting methods have a similar requirement for the ID variable (date or datetime information): it needs to be complete, with other words missing values in the time ID are not allowed. As such you will have to fill in these gaps for the time ID - for example by using PROC TIMESERIES: data have; set sashelp.air; if date="01JAN57"d then delete; if date="01SEP59"d then delete; run; proc timeseries data=have out=want; id date interval=month accumulate=total; var air; run; One the time id is intact, for UCM the following holds true (from documentation): "Embedded missing values in the dependent variable usually cause no problems in UCM modeling. However, no embedded missing values are allowed in the predictor variables." Depending on your business question at hand you may want to impute missing values of the dependent varialbe using PROC TIMESERIES for example (see setmissing option). Thanks, Udo ... View more

Thnx Udo i used your approach , modified a bit and it worked , thank you for the clarification ... View more

Propensity modeling is such a broad term. From your question it seems like what you really need is some clustering technique reference. In general I really like Data Preparation for Data Mining using SAS by Mamdouh Refaat. Many topics discussed in this book apply across the board to all commonly used techniques, including Cluster Analysis. My go-to book for clustering using SAS is actually SAS' training material from their Applied Clustering Techniques course. SAS has at least another couple of cluster related courses, which I have not attended and cannot really speak to. There is a Customer Segmentation using EM, and a Propensity Score Matching one. G ... View more

Thank you for the clarifications ... View more

There is no automatic way.  It takes defining your criteria and checking them.  Even then, the best you can do (usually) is categorical vs. continuous (integer) vs. continuous (noninteger).  Sometimes you might want to distinguish binary from the other possibilities as well, but you may have to check a larger sample to be confident of a variable being binary. If a variable takes on 20 different values, must it be continuous (not categorical)?  What is the limit? If a variable takes on noninteger values, must it be continuous? Any rules you come up with will always have exceptions.  For example, you may get a set of integers that represent percentiles, and have 100 possible values.  It would be good practice to keep lists of variables that you know about:  variables that are always categorical (no matter how many values they take on), and variables that are always continuous (no matter how few values they take on). The checking is usually done on a sample of observations, but I have typically used thousands rather than hundreds of observations. Good luck. ... View more

Hi - Not sure if this is what you were asking for, but check out the code below. Thanks, Udo DATA WORK.airline;     LENGTH         Passengers         8         DATE               8 ;     FORMAT         Passengers       BEST10.         DATE             DATE9. ;     INFORMAT         Passengers       BEST10.         DATE             DATE9. ;     INPUT         Passengers DATE; if date="01SEP2001"d then event=1; else event=0; DATALINES; 37208190 01JAN1990 36078394 01FEB1990 44154535 01MAR1990 41355578 01APR1990 41234923 01MAY1990 44190605 01JUN1990 46036310 01JUL1990 48866148 01AUG1990 38989264 01SEP1990 41247525 01OCT1990 39346113 01NOV1990 39342008 01DEC1990 36716670 01JAN1991 33688621 01FEB1991 39477477 01MAR1991 39878137 01APR1991 41018839 01MAY1991 43166181 01JUN1991 45727485 01JUL1991 47802583 01AUG1991 38628284 01SEP1991 41170282 01OCT1991 37451867 01NOV1991 41159310 01DEC1991 36719242 01JAN1992 35757887 01FEB1992 41861145 01MAR1992 39651142 01APR1992 41800746 01MAY1992 47431207 01JUN1992 51806387 01JUL1992 53163782 01AUG1992 42510606 01SEP1992 42253840 01OCT1992 39566414 01NOV1992 40849861 01DEC1992 38529065 01JAN1993 36539132 01FEB1993 43775242 01MAR1993 43125070 01APR1993 44300992 01MAY1993 46334965 01JUN1993 49482654 01JUL1993 50934128 01AUG1993 43364388 01SEP1993 45930858 01OCT1993 42769377 01NOV1993 43872406 01DEC1993 40665800 01JAN1994 39264392 01FEB1994 48963885 01MAR1994 46124425 01APR1994 47883199 01MAY1994 50554571 01JUN1994 54026538 01JUL1994 54735918 01AUG1994 46918744 01SEP1994 49220347 01OCT1994 46742038 01NOV1994 47700305 01DEC1994 44687190 01JAN1995 41896823 01FEB1995 50985658 01MAR1995 49212746 01APR1995 49824307 01MAY1995 52881186 01JUN1995 54957670 01JUL1995 56585046 01AUG1995 47709592 01SEP1995 50595784 01OCT1995 48310520 01NOV1995 49158213 01DEC1995 45722827 01JAN1996 47130093 01FEB1996 55909079 01MAR1996 52390127 01APR1996 53777332 01MAY1996 56095234 01JUN1996 58058659 01JUL1996 59802408 01AUG1996 49758328 01SEP1996 53679750 01OCT1996 48464209 01NOV1996 53181172 01DEC1996 49228750 01JAN1997 47152265 01FEB1997 58163010 01MAR1997 53944329 01APR1997 55635847 01MAY1997 58172771 01JUN1997 61153800 01JUL1997 61907945 01AUG1997 51761004 01SEP1997 55026915 01OCT1997 51524528 01NOV1997 53801076 01DEC1997 49493435 01JAN1998 47935244 01FEB1998 58134392 01MAR1998 56871713 01APR1998 57787100 01MAY1998 60218576 01JUN1998 64890982 01JUL1998 62715323 01AUG1998 52507440 01SEP1998 56988546 01OCT1998 53904572 01NOV1998 55301435 01DEC1998 50996487 01JAN1999 49598613 01FEB1999 61104493 01MAR1999 58753327 01APR1999 59026650 01MAY1999 62607968 01JUN1999 66663065 01JUL1999 65309471 01AUG1999 55473871 01SEP1999 60425711 01OCT1999 58067248 01NOV1999 56156443 01DEC1999 51750091 01JAN2000 53589785 01FEB2000 64948291 01MAR2000 61998314 01APR2000 64349644 01MAY2000 67807620 01JUN2000 69852555 01JUL2000 68484270 01AUG2000 58107878 01SEP2000 62153577 01OCT2000 60213479 01NOV2000 57943798 01DEC2000 55108316 01JAN2001 52915977 01FEB2001 65068044 01MAR2001 62373365 01APR2001 62622321 01MAY2001 66193520 01JUN2001 69529176 01JUL2001 70133361 01AUG2001 38730506 01SEP2001 47746624 01OCT2001 48250267 01NOV2001 49816945 01DEC2001 47844272 01JAN2002 47055580 01FEB2002 59210469 01MAR2002 55059178 01APR2002 57283840 01MAY2002 60462592 01JUN2002 63263816 01JUL2002 63450360 01AUG2002 49890724 01SEP2002 58113746 01OCT2002 54351302 01NOV2002 60266707 01DEC2002 53399694 01JAN2003 50187136 01FEB2003 60504638 01MAR2003 56394469 01APR2003 58730501 01MAY2003 63367220 01JUN2003 68449976 01JUL2003 66720119 01AUG2003 54119709 01SEP2003 60225641 01OCT2003 56951134 01NOV2003 49866044.9 01DEC2003 44223734 01JAN2004 45343339 01FEB2004 ;run; proc ucm data = airline;    id date interval = month;    model passengers = event;    irregular;    level;    slope var = 0 noest;    season length = 12 type=trig;    estimate outest=est;    forecast back=12 lead=12 plot=(decomp forecasts) outfor=for; run; data _null_; set est; if upcase(component) = upcase("event") then do;   call symput('eventParm', estimate);   call symput('eventParmStd', std); end; run; data adjust(keep=date event contrib stdContrib); set airline(keep=date event); est = symgetn('eventParm');     std = symgetn('eventParmStd');     contrib = est*event;     stdContrib = std*abs(event); run; ... View more

Hello - Can you please provide an example of what your are trying to archive? Something like here is the data I have, and this is the output I would like to see? For example, is is not clear to me if you want to introduce a date variable to your data or if there is a time stamp already. What is your analysis variable (e.g. the measurement which you would like to observe over time)? Should all series start and end at the same date and which frequency do you have in mind? Thanks, Udo ... View more

Ok, So for the second method i need the Outstat data set, that i understand. Regarding the first method which will be my SEED dataset, the OUTTREE from PROC CLUSTER or the OUT from PROC TREE? Thnx in advance Rick ... View more

I did some research but could use a confirmation. 1) Cluster procedure: proc cluster data = /* WHICH DATA */              method=ward /* WHAT LINKAGE */              outtree=   ccc pseudo print=15/* OUTPUT DATA INCLUDING SOLUTION */;              id CHOSEN ID /*VAR THAT WILL BE CLUSTERED */; var   /*VARS TO BE CLUSTERED */; run; /*Produce tree so as to see the shape of solution */ ods graphics on; proc tree nclusters=5 /* HOW MANY CLUSTERS */           data= /* OUTPUT DATA OF STEP 1 IS INPUT DATA IN STEP 2 */           out=/* FINAL DATA INCLUDING SOLUTION */; id chosen ID; run; ods graphics off; proc sort data = out=; by Cluster; run; proc means data =; output out=; run; proc fastclus data=<newdata> maxclusters=<nclusters> seed=<centroids> maxiter=0 out=<scored> My question here is how the proc means syntax should be cause it doesnt make sense to use the output from the tree that contains only the cluster and ID. 2) Fastclus proc fastclus data= outstat= maxclusters=5; var ; id chosen ID; run; proc means data= proc fastclus instat= out=score data; run; Same here as above  regarding the proc means syntax, what should be my input? Thnx in advance ... View more

Hi Steve, thank you for your reply, its a start non the less KR ... View more

It looks like you could use Discriminating Analysis ,since you have already created 6 clusters. Ksharp ... View more

You never did post a couple of records from each of your two types of files (i.e., those that do and those that do not have header records).  It is quite possible to write code that can determine whether or not a header record exists and do all of the imports in one pass. ... View more

Yes, this sounds a lot like something you could use the entity resolution functions in DataFlux Data Management Studio for. ... View more