@albanD Thanks for the info. Yeah, that makes sense. You are totally right that this kind of forecasting can be handled differently depending on the scenario, but I figured I would check anyway. Like I said, it is not that hard to implement. I guess my only question would be if there is an easy way to shift the elements in a tensor instead of having to create a copy of the original tensor with shifted elements each time. Like could I use the torch.roll() function, and then replace say the last 3 elements. Or does roll() also create copies.
I can think about the GPU thing. I am still new to Pytorch, so still just trying to figure out the balance between CPU and GPU. But you are right, it is probably best to just run the prediction routine on the GPU and then pull back the final results.
Thanks again for your input and very prompt response :).