I have a dataset which contains a large number of time-series which have been filtered with acausal Butterworth filters. For a real-time application, I can only use causally filtered data. Is it possible to turn the output of an acausal Butterworth filter into what would have been obtained if the original time-series had been run through a causal Butterworth filter? (I do not have the original, unfiltered, time-series)
I do know the order of the filter that was applied, the corner freuquencies, and some parameter 'nroll' of which I don't know what it is ... (nroll is usually equal 2.5)