  Dynamic Mode Decomposition (DMD) is a data-driven modeling tool that
generates a model from spatio-temporal data. The data needs to be as clean as
possible for DMD to come up with a faithful model. We review a few
data-filtering methods to be integrated with DMD and test them on datasets of
varying complexity. The impact of SNR on these methods and the error variation
in the DMD model due to each method are observed and discussed.
