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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.

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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.

Just write your GP model in Pyro or something like that.

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