When is it convenient dropping duplicates when performing Time Series Prediction? I have this Dataset.

 Head(df):

          ï..date   store item  qty    unit_price item_category 
 1220   2017-03-15    38  295   255      13.30            13 
 1453   2017-03-15    41   43   291      10.08            13 
 3181   2017-03-15    75  186   324      28.59            13 
 3541   2017-03-15    42  295   133      13.30            13 
 3624   2017-03-15    56  127   969      55.23            13 
 4657   2017-03-15    52   75   121      27.23            13 
 4702   2017-03-15    13  413    75      18.55            13 


There are clearly a lot of duplicates in the date column (since it depends on the stores and the items , that was expected), and since my task is to make a prediction and return predicted values in numeric of the qty variable - the target prediction variable "qty", represents the total quantities sold at the ITEM/DAY level- i think i can drop the duplicated dates (I'll be using the ARIMA model) and keep only the date , item and qty column for my prediction .

I wanted to know if my approach was right, any suggestion would be much appreciated , Thank you.
According to me, time-series ML Model are bit different than other routine ML  models. As time-series model is based upon the sequence of previous values, dropping a Date may impact your solution.

Specially in this case where sale is definitely dependent upon the particular day.
For eg consider store 38 is located in Area where on "2017-03-15" is holiday. So sale of store 38 will be comparatively high in comparison to other store and so on.

Rest you can try two different models one with Date and another without Date and compare the results as well.