# Prediction method when the time series is not sequential?

I have multivariate time series data consisting of monthly sales of contraceptives at various delivery sites in a certain country, between January 2016 and June 2019. The data looks as follows:

The task in hand is to predict the average monthly sales (stock_distributed) for July, August and September (row month) for 2019. However, the data is not a multivariate time series data (not sequential), and the predicted results should fit in this table:

As you can see the predictions are based on combinations of different explanatory variables. My question is: what is the most appropriate deep learning method that would allow me to predict the monthly sales as combinations of the four explanatory variables?