An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows

2022/06/29 by Kaan Gökcesu, Gokcesu, Kaan, Hakan Gökcesu +1
Mathematics · #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC) #Signal Processing (eess.SP) #Statistical Methods and Inference #Statistical and numerical algorithms #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2206.14749

openalex publication_date 2022/06/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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