It is common to give some multivariate time series to a Neural Network and get predictions for each individual time series. But my question is, does the NN take all series in consideration when creating the forecasts for one series?
Here is an example:
When trying to predict the number of Deceased, Infected, Recovered and Healthy people in a country it is possible to give the following data to a NN.
Deceased | Infected | Recovered | Healthy Day 1 10 | 20 | 10 | 60 Day 2 15 | 30 | 15 | 40 Day 3 20 | 40 | 20 | 20 Day 4 25 | 50 | 25 | 0
A naïve model would output some results that follow the trend present in the data:
Deceased | Infected | Recovered | Healthy Day 5 30 | 60 | 30 | -20
This NN produced an output for each series without taking each other into consideration. A more robust model would 'realise' that there cannot be a negative number of healthy people (or any king of people) and create an output like this:
Deceased | Infected | Recovered | Healthy Day 5 30 | 40 | 30 | 0
I have read about making the model always output positive values here and here. But this is different in the sense that I want to know how to make a NN take into consideration the other time series then making predictions.
Is a multivariate model simply making predictions for each sequence in parallel or is it taking all variables in a time step into consideration?