Formulation of Weighted Average Smoothing as a Projection of the Origin onto a Convex Polytope
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Abstract (arXiv)
Our study focuses on determining the best weight windows for a weighted moving average smoother under squared loss. We show that there exists an optimal weight window that is symmetrical around its center. We study the class of tapered weight windows, which decrease in weight as they move away from the center. We formulate the corresponding least squares problem as a quadratic program and finally as a projection of the origin onto a convex polytope. Additionally, we provide some analytical solutions to the best window when some conditions are met on the input data.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 61 references that are in the map. First posted on arXiv 19 Mar 2023.
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10 of 61 references.
References · 10
- 2021Convex optimizationcited by 2,515
- 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
- 2022Nonconvex Extension of Generalized Huber Loss for Robust Learning and Pseudo-Mode Statisticscited by 8
- 2016The Problem of Projecting the Origin of Euclidean Space onto the Convex Polyhedroncited by 2
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