Hello, I have moderate background in time series analysis. I'm trying to do an intervention modeling using proc arima. The model that I have postulated is an gradual permanent change such as shown below. See below for an example code. The problem that I'm facing is when I'm visualizing using ratio function using iml, I'm not able to get the appropriate effect. Any help in representing the gradual permanent change using transfer function in proc arima as well as visualizing using ratio function in proc arima would be greatly appreciated. ********************************************************************************************************************; ** Input Raw Data  **; ********************************************************************************************************************; data temp;   input cd @@;   datalines; 0 0 0 0 0 0.1 0.3 0.5 0.6 0.7 0.75 0.78 0.79 0.8 0.805 0.806 0.807 0.808 0.808 0.808 0.808 ; run; ********************************************************************************************************************; ** Create an indicator flag for Intervention  **; ********************************************************************************************************************; data temp;   set temp;   if _n_ > 5 then step = 1;   else step = 0; run; *****************************************************************************************************************; ** Identify the transfer function using cross correlation plot.  **; ********************************************************************************************************************; ods graphics on; proc arima data=temp plots=all;       identify var=cd crosscorr = (step); /*Identify Transfer Function by CCF*/    estimate input = (/(1)step) method = ml outest = mm1; /*Estimate Model using Transfer Function Gradual Permanent Change*/        run; ods graphics off; ********************************************************************************************************************; **  Extract Intervention for plotting  **; ********************************************************************************************************************; %let NUMLIST = 0.22; %let DENLIST = -0.74; %let nlags = 40; proc iml;          Phi={&NUMLIST};          Theta={1 &DENLIST};          Psi=ratio(Theta,Phi,&NLAGS);          numLag=&NLAGS;               Lag=1:numLag;          Psi=Psi[1,1:numLag];          Y=Lag`||Psi`;          oname={'Lag' 'Psi'};          create temptransfer from Y[colname=oname];          append from Y;          close temptransfer; run; ods graphics on; proc sgplot data=temptransfer;             vbar Lag / Response=Psi Stat=sum;             refline 0 / axis=y; run; ods graphics off; ... View more

I recently saw in the international institute of forecasters conference abstarct SAS has an experimental procedure called proc esmx for state space exponential smoothing? has this been relased yet or will I see them in the future release > Thanks ... View more

I have a forecasting task at hand that has multiple seasonality. As an example, hour of the day, day of the week, week of the month, and month of the year. Is there a SAS forecast procedure that can handle multiple seasonality, I have access to SAS forecast server, SAS /ETS. I tries using Proc UCM but it is very slow. Thanks for your help, much appreciated ... View more

I have dataset with 3000 customers and each customer has 120 observations. I was trying to build predictive model for each customer. There are several SAS procedures such as proc GLMSELECT or proc GLM that support by group processing. I was wondering if SAS enterprise miner has the option of "by" group processing ? If so could you please post an example or an illustraction on how this could be done. I checked the SAS EM user notes, and was not sucessful. Thanks ... View more

I would like to create a winsorized mean across variable and store it as a new variable in the original data set. Is there a way in SAS to get winsorized mean across variables ? Below is the code for generating input dataset. data have;   input var1 $ var var3; datalines; _1 10 12 _2 20 14 _3 30 16 _4 40 18 _5 50 20 _6 60 22 _7 70 24 _8 80 26 ; run; I know proc IML has the facility to do winsorized mean for each column, I need to do something similar but for each row instead of column. The desired output dataset would have all the original variables: var1, var2, var3 and also the calculated winsorized mean for each row. proc iml;     use have;     read all var _NUM_ into X;     read all var _CHAR_ into C;     close have;     mean = mean(x[,],"winsorized", 1);     create want from mean;   append from mean; quit; thanks for your help ... View more