An Auto-Regressive Formulation for Smoothing and Moving Mean with Exponentially Tapered Windows
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 60 references that are in the map. First posted on arXiv 29 Jun 2022.
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10 of 60 references.
References · 10
- 2018Flow Smoothing and Denoising: Graph Signal Processing in the Edge-Spacecited by 37
- 2021Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statisticscited by 32
- 2018Sequential Outlier Detection based on Incremental Decision Treescited by 21
- 2021Optimally Efficient Sequential Calibration of Binary Classifiers to Minimize Classification Errorcited by 17
- 2020Recursive Experts: An Efficient Optimal Mixture of Learning Systems in Dynamic Environmentscited by 15
- 2021Nonparametric Extrema Analysis in Time Series for Envelope Extraction, Peak Detection and Clusteringcited by 12
- 2021Optimal and Efficient Algorithms for General Mixable Losses against Switching Oraclescited by 10
- 2021Cumulative Regret Analysis of the Piyavskii--Shubert Algorithm and Its Variants for Global Optimizationcited by 9
- 2022Nonconvex Extension of Generalized Huber Loss for Robust Learning and Pseudo-Mode Statisticscited by 8
- 2022Smoothing with the Best Rectangle Window is Optimal for All Tapered Rectangle Windowscited by 3
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