An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows
This work provides an incremental improvement for time-series analysis by enhancing smoothing techniques with auto-regressive models.
The authors tackled the problem of smoothing time-series by developing an auto-regressive formulation that modifies the objective function of traditional moving mean smoothers, resulting in moving means with exponentially tapered windows that enforce higher smoothing while maintaining efficiency.
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.