Feature Selection on Aggregated Targetdata
It seems to me that there's a bit of confusion between these two steps:


Designing the problem and preparing the data accordingly
Applying ML methods to the data (feature selection, etc.)


The two steps must be distinct: any aggregation must take place during step 1 so that at the end of step 1 the dataset is fixed. 

In your example in step 1 you decided to predict the total volume of sale across  stores given the week and price per unit. Note that you could also choose other options:


average volume of sale by store as target
keep one row for each store: depending on the goal, sometimes it's ok to have inconsistencies in the data, regression can deal with these.
you could decide that an instance for a store spans over the N past weeks and provide the sales for these weeks, the target being the week after that (that would probably help btw). 
...


And of course you can do this as many times as needed for studying different problems, each time obtaining a different version of the data which represents a particular problem. It's only once you have formally defined your problem and formatted your data for it that the ML part starts. For instance stepwise regression (or any other technique) cannot work if the data is modified/aggregated during the steps.