Very nice @Sleort, thanks for putting a package together and for adopting the evaluate interface we discussed in another thread 
I would like to share with you something that has great overlap, which is the concept of variograms. A variogram is basically a constant (the sill) minus the autocorrelation, but it is more general in the sense that we can still use them to create models that aren’t necessarily stationary. There is also an efficient procedure to calculate variograms in more than one dimension, not only time series. I have implementations of variograms in GeoStats.jl, this Jupyter notebook illustrates more or less the modeling: http://nbviewer.jupyter.org/github/juliohm/GeoStats.jl/blob/master/examples/VariogramModeling.ipynb
Feel free to play with them, compare results, and suggest improvements. Good to have more people involved with autocorrelation-related work. 