  Dynamic Mode Decomposition (DMD) is a data based modeling tool that
identifies a matrix to map a quantity at some time instant to the same quantity
in future. We design a new version which we call Adaptive Dynamic Mode
Decomposition (ADMD) that utilizes time delay coordinates, projection methods
and filters as per the nature of the data to create a model for the available
problem. Filters are very effective in reducing the rank of high-dimensional
dataset. We have incorporated 'discrete Fourier transform' and 'augmented
lagrangian multiplier' as filters in our method. The proposed ADMD is tested on
several datasets of varying complexities and its performance appears to be
promising.
