I am trying to find large datasets with inherently sparse covariance matrices, to be tested with our algorithm. Basically, we will take the sample covariance matrix and enforce some structured sparsity (such as banding or hard thresholding) and adjusting it back to a positive semidefinite matrix.
The weird thing is that I am having the hardest time finding real data that actually has sparse covariance! I think the place to look is something with a band pattern, such as with shifted time series, but I can't think of the right application for this. Does anyone have any suggestions?
Either abstract concepts or links to data are appreciated; I'm getting good at finding data once I know what application I'm targeting but I'm blanking out now on what application to use!