An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows
Machine Learning
2022-06-30 v1 Signal Processing
Optimization and Control
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2206.14749,
title = {An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows},
author = {Kaan Gokcesu and Hakan Gokcesu},
journal= {arXiv preprint arXiv:2206.14749},
year = {2022}
}