Why are Gradient Boosting Models poor at making predictions? • /r/MachineLearning

Your question is confusing, because in machine learning we call any output of the model a prediction. But I gather you are talking about extrapolation. As in you have trained on values for t 10 and want to know the future value at t 10. If you want to use GBM or RF, all you need to do is to make sure your input features are in the same range in training as in testing. So you won't be able to have a year feature where the training data has values in (1970,2010) and predict for 2016. But an input dimension that encodes the weekday would work (if the weekday is meaningful for your predictions), because you are dealing with the same Mon-Sun range in training and testing.