The fundamental problem you face is clearly defining your observations. In a time series, you will end up with time as the x axis, corresponding to Date in your source data. What will be the y axis? Aggregate sales of all products by all stores per date? Or is it something else? What your time series will show is essential to being able to advise on how the data should be organized.
Second, you will get a much better response if you include a reproducible example, called a reprex, and identify, in this case the origin of (fable) (https://github.com/tidyverts/fable), which is not on CRAN.
Third, read the documentation for fable closely. It expects input as a tibble. To transform your data into a tibble, you need to understand that the underlying principle is one row per characteristic of interest, whether it be store_number, product_number or some constructed variable from your data. tibble requires it and fable requires a tibble object.
This is why the y axis identification is so critical.