by JoshD
Last Updated September 18, 2018 11:19 AM

My dataset represents a field evolving over time, so has dimensions [X,Y,T].

I would like to generate synthetic data with the same autocorrelation structure and spatial correlations as the real data (to validate the significance of some Hidden Markov clustering) but I am not sure how to do this.

For each single gridpoint with dimension T, I can get the desired autocorrelation through Fourier phase randomisation. However a naive attempt to then apply the same method in X and then Y afterwards gives incorrect results.

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