I suppose just a hint as to whether this is appropriate; it’s my first try diving into the autograd stuff:
If providing my own gradients, I need to calculate d_loss / d_params, correct? So for each element in my learnable time series, what is the ratio of loss delta versus the adjustment delta.
What amount of smoothness is required to be smooth enough for Adam/SGD to function? This would, I believe, directly map to how many iterations of gradient sampling are required.