I am working on a time series prediction problem. I am using keras models for machine learning.
For this prediction, weather variables are used as input. They can be of two types: forecasted and actual. I have acquired both these types of data of a considerable amount of time and I want to train and test my model on the data. My question is:
1) Should I use forecasted weather variables OR actual weather variables in input while training? (consider that only forecasted weather variables will be available at model inferencing time).
2) Same question 1 for testing.
Is there a rule or general practice regarding the above questions? If yes, I would like to know that.