I can't seem to find much on smoothing across a large number of correlated sparse time series. I have some working ideas, but is there a standard approach?
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That might work well with one experiment I've been trying for the sparseness; modeling the process as two parts; a poisson for the run of zeros & a gaussian for the non zero values.
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ok yeah, so model the parameters of the individual poissons and gaussians as generated by some distributions that are themselves globally parameterized...
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note: I think I'm over my head in terms of model correctness, but I'm the master of empirical testing so I should be ok :)
End of conversation
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