Elastic trend filtering

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2022-04-26 DOI:10.1515/ijnsns-2021-0237

Juyoung Jeong, Y. Jung, S. Yun

引用次数: 1

Abstract

Abstract Trend filtering aims to estimate underlying trends in time series data, which is necessary to investigate data in a variety of disciplines. We propose a new method called elastic trend filtering. The proposed method combines ℓ 2 and ℓ 1 norm penalties to exploit the benefits and strengths of Hodrick–Prescott and ℓ 1 trend filterings. We apply the alternating direction method of multipliers for its efficient computation and numerical experiments show the soundness and efficiency of the proposed method. We further apply the proposed method to graph cases for potential applications and suggest a trend filtering for its variance estimate.

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弹性趋势滤波

趋势过滤的目的是估计时间序列数据的潜在趋势，这是研究各种学科数据所必需的。我们提出了一种新的方法——弹性趋势滤波。该方法结合了l2范数惩罚和l1范数惩罚，利用了Hodrick-Prescott和l1趋势滤波的优点和优势。采用乘法器的交替方向法计算效率高，数值实验证明了该方法的有效性。我们进一步将所提出的方法应用于潜在应用的图形案例，并建议对其方差估计进行趋势过滤。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.