Hi @gibi_serpro , you can't change the default nlp concepts, but you can reference them in a custom concept node which you then add your custom classifier / LITI rules to. Thanks, Harry ... View more

Hi. If I've understood your question correctly, this should be available automatically using the 'Frequency' attribute. Just set the level of aggregation needed.    Take for example the cars dataset, frequency updates depending on what you add to the list table.     Alternatively you can create a calculation manually using a formula like:   Hope that helps! ... View more

Stationarity in a time series effectively means the series fluctuates around a given mean for the series, this is a useful property for creating a reliable forecast because you are modelling the fluctuation of the series around the mean, not the series trend. When you have non-stationarity such as an upward trend in a stock price, for example, typically you difference it to make the series stationary. This is where models such as ARIMA are useful as they encapsulate three important aspects of time series forecasting: Autocorrelation (AR): how does x(t) depend on x(t-1), x(t-2), x(t-n)... Integration (I): how does the series need to be differenced to make it stationary Moving Average (MA): for how long do exogenous shocks impact the model not captured in the series autocorrelation ... View more

Hi @DavidLie ,    You didn't specify but I assume the output of each loop is the regression coefficients and fit statistics.    Please see below an example macro which takes the input data and preferred step size to create a loop of regressions which get collapsed into a single performance table. Is that what you were after?   *Create Data; data have; input x1 x2 y ; datalines; 69 0.901225752 0.535917754 26 0.979065064 0.328516725 21 0.462043623 0.928984209 70 0.372254307 0.020944067 26 0.70178162 0.015753384 22 0.755669857 0.287674489 45 0.621848244 0.876177263 23 0.049950103 0.524321342 75 0.323187042 0.458245799 62 0.988737694 0.528055761 59 0.047655873 0.637754194 59 0.499943648 0.997049298 25 0.597043632 0.556921095 92 0.679854964 0.433165676 29 0.686543133 0.720057785 35 0.861474371 0.344762962 79 0.591574823 0.876250534 53 0.741976868 0.430488835 71 0.146723447 0.605425714 75 0.435811738 0.861299126 96 0.785859466 0.717045755 99 0.520055579 0.784497337 58 0.394083913 0.712998746 61 0.875958722 0.667760878 15 0.561292166 0.836592031 56 0.652887463 0.236309836 9 0.344428535 0.224147253 6 0.785126736 0.435528686 72 0.847421203 0.160322663 78 0.663642034 0.466674263 83 0.333921138 0.029331531 77 0.486751999 0.706800698 92 0.801502963 0.788823137 9 0.578615463 0.347239912 41 0.053458445 0.094070384 68 0.251134914 0.103399917 2 0.49184594 0.102494486 97 0.120083405 0.055981267 78 0.5423958 0.181672902 17 0.834739212 0.142969285 ; run; *Define Macro; %macro regressionLoop(input,step,out); %let tblin=work.have; *add rownumber to input table; data indx; set &input; rownum+1; run; *get row count; proc sql noprint; select count(*) into :numRecs from &input;quit; %let stepping = %eval(&step - 1); *derive query parameters; data ranges(keep=start_num end_num); cnt = &step; do while(cnt lt &numRecs+1); start_num+1; end_num = start_num + &stepping; cnt+1; output; end; run; *get number of loops needed by step; proc sql noprint; select count(*) into :numQueries from ranges;quit; %put("starting loop"); %put("number of loops: &numQueries"); *loop build regressions; %do i=1 %to &numQueries; %put("loop number &i"); *get upper limit; proc sql noprint; select end_num into :upper from ranges where start_num = &&i.;quit; *get dataset based on range; proc sql noprint; create table regIn as select x1,x2,y from indx where rownum between &&i. and &upper.;quit; *generate regression; proc reg data=regIn alpha=0.05 outest=outest noprint; model y=x1 x2/ aic bic edf; run; quit; data reg_&&i.; set outest; _MODEL_ = "Range &&i. - %cmpres(&upper.)"; run; %end; %put("ending loop"); *collect reg tables into output table; data &out; set reg_1 - reg_%cmpres(&numQueries.); run; %mend regressionLoop; *Call Macro; %regressionLoop(have,20,out_stats);   Output looks something like this:   Thanks Harry ... View more

Hi @smm662002 , do you need the job to open in a new window or pop-up? You can control the Job Execution to not pop-out by specifying target=_self. You have this as a parameter in the job definition and add it to the URL you call in the VA web content canvas object. Here's a community paper with some more information: https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2020/4185-2020.pdf  ... View more

Hi @stevenyan0127 , is this what you're after? %macro dateGenerator(start,end,multi,out); *define date range; %let start_date=&start; %let end_date=&end; *generate list of dates; data Dates; date="&start_date"d; do while (date<="&end_date"d); output; date=intnx('day', date, 1, 's'); end; format date date9.; run; *initialise base table; data dates_out; set dates; run; *Set counter from desired multiplier; %let counter=%SYSEVALF(&multi - 1); *loop through multiplier adding dates table; %do i=1 %to &counter; data dates_out; set dates_out dates; run; %end; *create output table with rownumber; data &out; set dates_out; rownum+1; run; %mend; %dateGenerator(01Jan2008,10Jan2008,10,myDates); I've used your original code and effectively put it into a loop which multiplies the number of records.    ... View more

Hi @chirag1401 , Have you tried the INTCK function? https://blogs.sas.com/content/iml/2017/05/15/intck-intnx-intervals-sas.html. You can set the time difference in days between the two dates.  ... View more

Hi @luca87 PROC CAS works against in-memory CAS tables. You'll need to load the sashelp.cars table into memory first.    E.g.    libname casuser cas; data casuser.cars; set sashelp.cars; run; %let table=casuser.cars; ... View more

Hi @pacman94 ,   Is this what you're after? *Load data; data work.old; length id instance 8 group term term_2 $20; input id group instance term term_2 ; infile datalines dlm="," dsd; datalines; 1, 1, 1, AAAA, one 1, 2 indirect, 2, CCCC, seven 1, 2 indirect, 3, DDDD, seven 2, 1, 1, ASFD, two 2, 2 indirect, 2, DDA, three 3, 2 direct, 1, ERQE, one 4, 1, 1, ABCD, seven 4, 1, 2, ABCD, five ; data work.new; length id instance 8 group term term_2 $20; input id group instance term term_2 ; infile datalines dlm="," dsd; datalines; 1, 1, 1, AAAA, one 1, 2 indirect, 2, CCCC, seven 1, 2 indirect, 3, DDDD, seven 1, 2 indirect, 4, DFDD, two 2, 1, 1, ASFD, two 2, 2 indirect, 2, DDA, three 3, 2 direct, 1, ERQE, one 4, 1, 1, ABCD, seven 4, 1, 2, ABCD, five 4, 2 direct, 3, CCCC, one 5, 1, 1, ERQE, seven ; *Find new records; proc sql; create table work.flag_1 as select *, 1 as flagT from ( select * from work.new except select * from work.old); quit; *Add flag 0 to old records; data work.old; set work.old; flagT = 0; run; *Merge tables; data work.want; set work.flag_1 work.old; run;   ... View more

Hi @Rose2  you can do this within the datastep using an IF statement      data work.cars_short; set sashelp.cars; if make in ('Audi','BMW','Mercedes-Benz') then make_short = 'Luxury German'; else make_short = make; run; You could also use a CASE statement via PROC SQL. Hope that helps Thanks Harry ... View more