Clustering stores based on weekly data
You basically have data with a panel structure ($i=$shops, $t=$ weeks). So for a simple clustering problem, you need to "tell" the algorithm that observations for each $i$ (shops) are not independent. 

I think you are on the right way to treat each week $t$ as an own variable/feature here. In this case you would have 416 features and 1000 observations. This should be "okay", but especially with non-supervised problems, it is up to a try.

In any case, I would also try to do a little aggregation on the data (over time). Maybe there are good reasons to do this by month or quarter (or some other sort of "season"), meaning to sum up the weekly values over some period of time. Say you do this for quarters, you would have "only" 32 features, which should be absolutely fine with 1000 observations. 

See also this post: https://stats.stackexchange.com/questions/88320/cluster-analysis-on-panel-data