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# neural-forecast
s
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c
Hi @Manuel Chabier Escolá Pérez! If I understand correctly, this is your setting: 1. You have two variables, namely 
X_1
and 
X_2
. 2. At a timestamp 
t
you have previous values and forecasts for both variables. 3. The values of the variables update, so at 
t
the value of 
t+2
for variable 
X_1
is A. But then, at 
t+1
you update the previous value (still in the future, now one timestamp ahead), to A'. Is this setting correct?
In the library you can specify two types of temporal exogenous variables: 
historic
and 
future
. For the former, the model to make a forecast at 
t
can only access historic values 
<=t
, and for the later it can use both historic and future values, up to 
t+horizon
. In this case both variables should be 
future
values, so models can access the forecasts.
Now for the updating problem, there is currently no way to incorporate this behavior during training, as each variable can only have one value at each timestamp. You will need to define the variable, for example, to be the "forecasts of 
X_1
done at timestamp t" (so taking values A in your diagram and so on).
During inference (test), you can simply create you own for loop to make forecasts at every desired timestamp, and update the values of your variables manually before doing the forecast. So it would be a loop with first updating the data: 
Y_df
and 
futr_df
, and then calling the 
predict
method.
Hope this helps! It is a lot of information so let me know if something is not clear!
m
Hi Cristian! Thank you very much. What you suggest about the training part was my plan B (i.e., You will need to define the variable, for example, to be the "forecasts of 
X_1
done at timestamp t"), but I wanted to make sure that there was not another approach. However, I had not thought about the loop for the testing set, and that is a very simple but great idea! I will try it. Thank you very much!
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