A lot of M3 datasets we use are high-frequency, with large seasonal inputs. Considering Gaussian Processes (GP) complexity is O(N^3), a careful study of their performance would be challenging.
Also... I'm not aware of any efficient GP Python implementations.
Just write your GP model in Pyro or something like that.
GPs over time series can leverage low-dimensional index sets for O(N lg N) fitting and inference. This can be done by interpolating the inputs onto a regular grid which admits Toeplitz kernels. See https://arxiv.org/abs/1503.01057.