Hello,
My question is "In order to stock the inventory, the store wants to analyze the sales data and predict the trend of total sales for each dress for an extended period of three more alternative days."
For this the supporting data that I have got is as follows :
Dress_ID | 29/8/2013 | 31/8/2013 | 2/9/2013 | 4/9/2013 | 6/9/2013 | 8/9/2013 |
1006032852 | 2114 | 2274 | 2491 | 2660 | 2727 | 2887 |
1212192089 | 151 | 275 | 570 | 750 | 813 | 1066 |
1190380701 | 6 | 7 | 7 | 7 | 8 | 8 |
966005983 | 1005 | 1128 | 1326 | 1455 | 1507 | 1621 |
876339541 | 996 | 1175 | 1304 | 1396 | 1432 | 1559 |
Not sure exactly which approach to use here or how to go about this problem.
Regards,
Aditya
The first thing to do is, of course, to transpose your dataset (once it has been imported into SAS; it can't be a SAS dataset at the moment as a date is not a a valid SAS column name) so you end up with three columns: ID, date, sales.
Then you need to calculate the difference from previous to current observation, so you get the daily sales; use the lag() function for this.
Once that is done, you can calculate average sales for certain timespans, or see how sales spread over a week, etc, etc.
This is a time series analysis so review the Procs under SAS ETS for a relevant method.
Hi,
Since you have only a few observations at your disposal, you can proceed as follows
1. Transpose your dataset
2. Sum the columns to obtain total sales
3 . use a simple linear regression on a time trend with constant
4. Calculate the predicted values
Yeah. Post it at Forecast Forum. There are many PROC you could use like : PROC ARIMA , PROC ESM .......
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