How do I arrange my data to predict 6 weeks of daily sales I have a data.table base that has many variables to use them to forecasting sales for the next 6 weeks of daily sales. In fact, all the database is arranged by date as you can see here.Note that here I just show you some of variables.

&gt; Data_train[order(Date)]
         Store DayOfWeek       Date Sales Customers Open Promo StateHoliday SchoolHoliday
      1:     1         2 2013-01-01     0         0    0     0            a             1
      2:     2         2 2013-01-01     0         0    0     0            a             1
      3:     3         2 2013-01-01     0         0    0     0            a             1
      4:     4         2 2013-01-01     0         0    0     0            a             1
      5:     5         2 2013-01-01     0         0    0     0            a             1
     ---                                                                                 
1017205:  1111         5 2015-07-31  5723       422    1     1            0             1
1017206:  1112         5 2015-07-31  9626       767    1     1            0             1
1017207:  1113         5 2015-07-31  7289       720    1     1            0             1
1017208:  1114         5 2015-07-31 27508      3745    1     1            0             1
1017209:  1115         5 2015-07-31  8680       538    1     1            0             1 .


My question is about the arrangement of the data according to the target goal. My problem is really about the Date variable. 
In fact, I suggest this path:


I sum all sales by each date (because I have many types of stores).
I order my database according to the Date 's ascending order.
I didn't need duplicated rows in the date variables so I delete them.


Just to show you the new base for considered variables. 

&gt; Data_train[,SumSaleseachDay:=sum(Sales),by=c('Date')][order(Date)][!duplicated(Date)][,-c('Sales','Customers'),with=FALSE]
     Store DayOfWeek       Date Open Promo StateHoliday SchoolHoliday SumSaleseachDay
  1:     1         2 2013-01-01    0     0            a             1           97235
  2:     1         3 2013-01-02    1     0            0             1         6949829
  3:     1         4 2013-01-03    1     0            0             1         6347820
  4:     1         5 2013-01-04    1     0            0             1         6638954
  5:     1         6 2013-01-05    1     0            0             1         5951593
 ---                                                                                 
938:     1         1 2015-07-27    1     1            0             1        10707292
939:     1         2 2015-07-28    1     1            0             1         9115073
940:     1         3 2015-07-29    1     1            0             1         8499962
941:     1         4 2015-07-30    1     1            0             1         8798854
942:     1         5 2015-07-31    1     1            0             1        10109742


ADDED INFORMATION: 
I have a database of 1017209 rows. And for each Store, I have its historic of  Sales between 2013-01-01 and 2015-07-31. And I have also 17 variables included to build the model.

The steps above just lead to forecast by day.

If I want to forecast for each Store and by day, what should I do?
thank you in advance!
I guess it depends on exactly what you want - do you want a forecast per store? If so you would need to leave the data set aggregated only to a store level. If not, and you want the overall sales by day, then you are OK to leave it aggregated.

Another consideration is whether there are many store-level features e.g. location, weather, local demographics. These features may be useful for the model to learn from and would lose resolution if store level data is aggregated, therefore leaving it un-aggregated would be better. The counter point to this is whether you have enough data for each store to learn representative trends! So it does get kind of circular and depends on the size of your data set.