About roushankumar

roushankumar · ‎04-11-2019

Hi Rick, Could you please help me with the issue here https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/proc-hpfengine-fails-to-make-predictions/m-p/550250#M3507 Thanks

roushankumar · ‎09-29-2018

Hi, I am stuck with a problem where I have two tables, one at the months and one at the weeks. Here's the format of the tables: Table1 Customer Date1 Sales 1 Jan2018 1110 1 Feb2018 1245 1 Mar2018 1320 1 Apr2018 1100 ... Table2 Customer Date2 1 01Jan2018 1 08Jan2018 1 15Jan2018 1 22Jan2018 1 29Jan2018 1 05Feb2018 1 12Feb2018 1 19Feb2018 1 26Feb2018 1 05Mar2018 ... I want to create a new column for sales in Table2 that will hold the disaggregated values of sales from Table1. I want to divide the sales by the number of days in that month and then assign the values to the weeks accordingly. Thus the sales in week 01Jan2018 is (1110/31)*7. The weeks that are in transition will get values from both the months. For example 29Jan2018 has 3 days in Jan2018 and 4 days in Feb2018. The sales of one day in Jan2018 is 1110/31 and the sales of one day in Feb2018 is 1245/28. So the sales in week 29Jan2018 will be 3*(1110/31) + 4*(1245/28) I want to do this for each distinct customer. The resulting table should be Result Table Customer Date Sales 1 01Jan2018 250.6 i.e (1110/31)*7 1 08Jan2018 250.6 1 15Jan2018 250.6 1 22Jan2018 250.6 1 29Jan2018 282.27 1 05Feb2018 311.25 1 12Feb2018 311.25 1 19Feb2018 311.25 1 26Feb2018 133.39 + 170.32 ... Thanks!

roushankumar · ‎09-25-2018

Thanks! Do you know what is the default seasonality if I specify method = ADDWINTERS? I could not find it anywhere in the documents.

roushankumar · ‎09-24-2018

Hi Rick, Could you please help me with the question in this link? https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Exception-with-Proc-Forecast-when-specifying-SEASONS-MONTH/m-p/498094 Thanks, Roushan

roushankumar · ‎09-22-2018

I have my data at weekly intervals. I wanted to use a Winters model and since proc esm did not let me choose the smoothing weights I turned to proc forecast. This procedure works fine until I specify seasons=month. Please help solve this or suggest an alternative way to implement Winters method with smoothing weights of my choice. I got the following exception when I used seasons = month in proc forecast ERROR: An exception has been encountered. Please contact technical support and provide them with the following traceback information: The SAS task name is [FORECAST] Segmentation Violation Traceback of the Exception: ERROR: An exception has been encountered. 107 The SAS System 17:44 Saturday, September 22, 2018 Please contact technical support and provide them with the following traceback information: The SAS task name is [FORECAST] Segmentation Violation Thanks!

roushankumar · ‎09-17-2018

Hi, I created a model of the following form proc transreg data=mydata outest=work.est; model BoxCox(Sales) = identity ( x1 x2 x3 x4 ); run; When I check the table work.est I do not see coefficients against x1 x2 x3 and x4. The column 'Variable' in work.est does not have any values. I do see columns 'Statistic' and 'Value' which have Error, Root RMSE etc. How do I score an external data using this model's coefficients? Since I have used a boxcox transformation, what transformation do I need to get the predictions in the original form? Thanks for the help!

roushankumar · ‎09-11-2018

Thanks! Most sas procedures have a 'by' command to create models by a specific class of variable. Does proc hpneural have anything similar? Going around in loops is taking a lot of time.

roushankumar · ‎09-11-2018

Hi, When I use your code proc hpneural data=frames; input pixel:; hidden 1000; /* first layer */ hidden 500; /* second layer */ target label / level=nom; train numtries=1 maxiter=5000; /* nthreads=number of cores you want to use */ /* if you have SAS HPA then you can use the nodes= */ /* option to use more than 1 machine - vroom, vroom! */ performance nthreads=12 details; score out=frames_score; run; Where can I see the activation functions of the hidden layer and output layer? Can I also specify which activation function I want to have for the first layer and which one I want for the second layer? Thanks!

roushankumar · ‎09-10-2018

I think I was not clear with my reply. If the sales from the last month came higher then the forecast starts from the higher sales level which is something I don't want. Regression does not have any autocorrelation so it is serving the purpose for that reason.

roushankumar · ‎09-10-2018

I have done this several times in open source platforms but I am new to SAS and haven't been able to find a way here. Such customized models have helped me model cases where 'you want to stabilize your model further' or 'you would rather have your predictions greater than actuals rather than lower'.

roushankumar · ‎09-10-2018

Thanks! If I set sales between -5 and +5 to zero in my training data would it be the same as finding the best fitting line such that minimal errors are not counted? Please explain.

roushankumar · ‎09-10-2018

I have a data in the form Sales Is_Discounted 20 1 10 0 25 1 9 0 18 0 I want to create a linear regression such that only error greater than absolute(5) is used to build model that predicts sales. So any error in the range [-5,5] is considered as zero error. Could you please provide the code or reference to do this? Thanks!

roushankumar · ‎09-10-2018

Thanks Paige! The reason I do not want to go for ARIMA or ESM is that these models are very much prone to level shifts. So, if the last months sales came higher these models start predicting from that higher level rather than coming back to the normal levels. Moreover even without the seasonality treatment, my accuracy with regression is slightly better than arima.

roushankumar · ‎09-10-2018

Hi, I have a weekly time series in the following format. category Product date Sales Is_Discounted a123 1224 12JUN2016 20 1 a123 1224 19JUN2016 10 0 a123 1224 26JUN2016 25 1 a123 1224 03JUL2016 19 0 a123 1224 10JUL2016 18 0 I need to predict weekly Sales for each unique combination of category and Product. I want to remove the seasonality from this time series and then create forecasts using proc reg and then add the seasonality back on the forecasts. I have been able to create regression model (using month number, week number and Is_Discounted as independent variables) without removing seasonality and its working fine, but I believe the seasonality in the data is impacting my accuracy with regression. I checked proc X12 but I do not understand how to use it to add the seasonality back after regression. Thanks for the help!

roushankumar · ‎09-07-2018

Thanks for looking into this. I did come across the resource earlier. The probability computations in the blog you suggested are done looking at one variable and keeping other variable constant. That will generate many probabilities for each row in my scoring data as I have many variables. The data I showed is a mocked up one. I believe logistic regression always generates coefficients with respect to a reference level and hence I said I am open to looking at any other algorithm. To your question 'Why does your output have two rows for Rick and Dan each? ' The NAME Rick and Dan will not be used in scoring but they will be used to join the scoring data with the right set of coefficients as we will have one set of coefficients for each NAME.

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Re: Linear regression that takes into account only error greater than ...

Disaggregating data from months to weeks

Re: Exception with Proc Forecast when specifying SEASONS = MONTH

Re: Scoring a data using the coefficients of proc transreg

Exception with Proc Forecast when specifying SEASONS = MONTH

Scoring a data using the coefficients of proc transreg

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Re: Linear regression that tales into account error greater than a spe...

Re: Scoring a data using the coefficients of proc transreg

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Re: Linear regression that takes into account only error greater than ...

Disaggregating data from months to weeks

Re: Exception with Proc Forecast when specifying SEASONS = MONTH

Re: Scoring a data using the coefficients of proc transreg

Exception with Proc Forecast when specifying SEASONS = MONTH

Scoring a data using the coefficients of proc transreg

Re: How to build a deep learning model in SAS Enterprise Miner

Re: How to build a deep learning model in SAS Enterprise Miner

Re: Removing trend and seasonality to create a regression and then add...

Re: Linear regression that tales into account error greater than a spe...

Re: Linear regression that tales into account error greater than a spe...

Linear regression that takes into account only error greater than a sp...

Re: Removing trend and seasonality to create a regression and then add...

Removing trend and seasonality to create a regression and then adding ...

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