Hello,
I wish to predict volume in every day , given a day,month,year, day in week (sunday/monday/...), and holiday indicator. ( total of 5 categorical variables) - base on history data of course.
basically I want to know the weight each of the variables and their values contribute to the volume.
I want to make that forcast as the volume is not necessarily spread uniqly (linear) throw the month. for example: if I'm at the begining of the month I might see a bad picture of what it may look at the end of the month.
So I'm looking for some beta-hats, or weights for the day/month/day_in_week/holiday.
will be glad to know if there is a proc that will do that.
Thanks in advance
Thanks for your answer. But the arima proc will give me the actual volume (by the forcast stament) and I only want to know the weighs of the day+month+year+ day in week+ holiday, so I can put into them my wish/ wanted volume for the future.
So the weights has to be precentage or somthing like that.
For example if I will know that Sunday has influence of 10% and my wish volume for the week is 400 and my actual volume on that Sunday was only 35 I know my volume for that day is not good. I hope I cleard myself better this time.
Thanks,
Hello -
Both ARIMA and UCM will not only give you forecasted values, but much more.
A nice feature of UCM is that it can "decompose" forecasts into their components - which will allow you to do the "what-if" type of scenario you are talking about.
Below you will find an example - hope this helps.
Thanks,
Udo
*first create future values for the masonry variable of sashelp.workers;
proc esm data=sashelp.workers out=masonry plot=forecasts;
id date interval=month;
forecast masonry / method=winters;
run;
*merge the 2 new data sets - note that for masonry it features forecasted values, which you can change later to assess the impact;
data total;
merge sashelp.workers(drop=masonry) masonry;
by date;
run;
*run UCM with mansonry, irregular and level (which is also called trend in UCM) as compoents;
*check out the outfor data set, which features the components of the forecasts;
*outest features the parameter estimates;
proc ucm data=total plots=smooth(decomp);
id date interval=month;
model electric=masonry;
irregular;
level;
estimate outest=outest;
forecast lead=12 outfor=outfor;
run;
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 16. Read more here about why you should contribute and what is in it for you!
Learn how to run multiple linear regression models with and without interactions, presented by SAS user Alex Chaplin.
Find more tutorials on the SAS Users YouTube channel.