I am using a support vector regression is order to get estimates of a variable y. I want to receive a probability distribution of my estimates and not just point estimates. I want to predict Gaussian Distributions for every point where the mean should be my point and I will also give a variance.
What I am thinking in order to avoid to use methods like Gaussian Processes or Probabilistic SVM is to use a history of N windows and to use the error as variance for my new estimates.
For example the std of the error of the last N windows is 0.5. Then the estimate if my prediction is 5 is N(5,0.5).
Is this approach right?