  We investigate an auto-regressive formulation for the problem of smoothing
time-series by manipulating the inherent objective function of the traditional
moving mean smoothers. Not only the auto-regressive smoothers enforce a higher
degree of smoothing, they are just as efficient as the traditional moving means
and can be optimized accordingly with respect to the input dataset.
Interestingly, the auto-regressive models result in moving means with
exponentially tapered windows.
