Thanks for quick reply and confirmation.   Is this feature expected in next release? VA 8.3  is good but some of the features which were developer friendly are still not there  but were in 7.4. Many things are not intuitive. Transition from 7.4 to 8.3 is long journey.     thanks Lokendra  ... View more

This question cannot be answered without you giving a lot more information. At least: 1. Are the tables sorted? indexed? 2. What sort of join is it? Cartesian, inner, left etc? 3. How many rows in each table? 4. Is(are) the join key(s) unique in either or both tables? 5. What is (roughly) the expected number of rows in the output table?       ... View more

Sorry, I wasn't including the bias terms in the number of parameter estimates.  You would have one of those for each hidden and output node as well. ... View more

Hi Lokendra,   The difference between reorder level and safety stock is the demand during lead time. PBR is defined as periods between two replenishment orders. If demand for PBR is considered like proc mirp, then orderupto level is identical to (safety stock to meet uncertainties) + (demand during PBR) + (demand during lead time)   Thanks, KDuck ... View more

When you posted your question, you saw this right below the editing window:   Stop right there! Before pressing POST, tick off this checklist. Does your post … ✔ Have a descriptive subject line, i.e., How do I ‘XYZ’? ✔ Use simple language and provide context? Definitely mention what version you’re on. ✔ Include code and example data? Consider using the SAS Syntax feature.             Please revisit your post in view of this, especially the third column. ... View more

Those notes arise because the same dummy parameter appears as both a constraint index and a summation index within the constraint.  I don't know your optimization model, but I suspect you want the following instead:   con RR_BINARY_CONSTRAINT{<ITEMID,STOREID> in Budget_dim}: sum{<(ITEMID),(STOREID),RR> in SKU_STORE_RR_DIM} SKU_STORE_RR_BIN[ITEMID,STOREID,RR]=1; con BUDGET_CONSTRAINT{<ITEMID,STOREID> in Budget_dim}: sum{<(ITEMID),(STOREID),RR> in SKU_STORE_RR_DIM} SKU_STORE_RR_BIN[ITEMID,STOREID,RR]*InvCost[ITEMID,STOREID,RR]*Avg_Onhand[ITEMID,STOREID,RR] <= Budget[ITEMID,STOREID]; At least that will avoid the notes.  To see whether the new declarations correctly express your desired model, you can use the EXPAND statement.   For more explanation of this implicit slice, see Manpower Planning and other examples in the Mathematical Programming Examples documentation. ... View more

To use PROC OPTMODEL to generate MPS or QPS, you must remove the nonlinear parts and handle them separately with PROC FCMP, as in this doc example: Example 3.6 Using Nonlinear Constraints   Alternatively, what you have shown so far can be linearized by taking logs, with LogDemand and LogRecommendedPrice as your decision variables:   LogDemand  = log(Constant) -1.8 (LogRecommendedPrice - log(FullPrice)) -1.5 (log(CompetitorPrice) - log(FullPrice))   LogRevenue = LogDemand + LogRecommendedPrice   If the full problem can be linearized, you can use the MILP solver in PROC OPTMODEL.  If you provide the full code and data, I'll take a look. ... View more

Hi Lokendra,   Regarding 1., if product sales are either highly stable, or highly erratic, then a flat line (constant) forecast can be most appropriate.   For highly stable demand this intuitively makes sense, and would be easy to explain to a client or other user of the forecast.   In your situation, since this is retail data, I suspect you are seeing highly erratic demand patterns (particularly if you are forecasting at a granular level like store/item/week).   A flat line forecast may not make intuitive sense for highly erratic demand because it doesn't appear to "fit" the history. However, if there is no underlying structure in the data -- that what you have are essentially random ups and downs -- then there is no pattern to be fit, and just forecasting the average demand makes sense. This appears to be the situation with your data because the constant forecast is performing best in the holdout sample.   A 2015 article in the Journal of Business Research ("Simple versus Complex Forecasting: The Evidence" by Green and Armstrong) found considerable evidence that simple models forecast better than complex models. Complex models often "overfit" the history -- mimicking the randomness rather than any real structure in the data and projecting it forward -- and thereby generate less accurate forecasts.   --Mike     ... View more

Dear,   Thanks for the detailed reply.  Client has used SAS Code node in Eminer because they are using CAB solution for the ABT development. The CAB has limited ability for different types of variable creation like you cant create flag variable unless you have in infomap and also categirical variables can not be regrouped. I suggested them to use replacement node for categorical variable and ineractive binning for numeric variables if they want to bin. Yes, you are right Eminer is for model development not for coding but client has CAB and Eminer as solution. They do not want to keep another piece of code in EG or base sas program seperately. Thanks for your help.   I have one more follow up question regarding SAS Code node and Score code.  Client has used Linkart method to convert the categorical values to numeric values using the SAS Code node. I do not think any other node can do this as far as I know.  They have used a macro for this purpose. I think SAS has shared this macro.  3 Variables which have been converted into numeric values using this method are in final model.  But in scoring code or optmized SAS code I do not see  any code which does this conversion. But data step for this conversion is present in SAS Code (unoptimzed one).  But when I score a new data I am getting correct values. Is the code for linkart conversion is implicit in Score node.   I tested with optmized score code in Base SAS program. I get missing warning for linkart converted variables which is ok because its not in the data.  I copied required code from unoptimized SAS code then I am getting same scorig output which I get in the Eminer score node. I am I not follwoing it correctly, please help me. I have to suggest the client if they use Linkart method then how should they score and how to ensure that there is no error in scoring.   thanks in advance  Lokendra ... View more

Hello lokendra_devangan_corecompete_com, Have you tried using parameters to create cascading prompts? By using cascading prompts, it will lessen the number of variables. Please reference the link attached below which will go into more detail about cascading prompts. Please notify me if you have any more questions regarding this issue.   https://communities.sas.com/t5/SAS-Visual-Analytics/Dependency-between-drop-down-list/td-p/164710   Best,  Meredith Bailey      ... View more

There are a couple resources to look at:   1) Detailed list of exam content: http://support.sas.com/certify/creds/aap3.html 2) Sample Questions: http://support.sas.com/certify/creds/samples/ ... View more

Hello - SAS Forecast Server is designed to tackle large scale forecasting problems - as such you will find functionality in this product which is specifically designed for these kinds of challenges, such as picking a champion model for each time series at hand from a wide range of statistical forecasting models automatically. The procedures you are referring to, such as ARIMA, ESM, and UCM, were implemented with a different purpose in mind, which is allowing a modeller to focus on a couple of time series only, and try to come up with the very best model, by fine tuning options manually. Of course there is overlap between the 2 domains, and things are not as black and white as my earlier statement may suggest. If you like to mimic SAS Forecast Server like behaviour with these procedures, you probably will have to write a lot of SAS code yourself. Thanks, Udo ... View more