汉克尔低秩逼近与补全在时间序列分析与预测中的应用综述

IF 0.3 4区数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Statistics and Its Interface Pub Date : 2023-04-13 DOI:10.4310/22-sii735

Jonathan Gillard, Konstantin Usevich

{"title":"汉克尔低秩逼近与补全在时间序列分析与预测中的应用综述","authors":"Jonathan Gillard, Konstantin Usevich","doi":"10.4310/22-sii735","DOIUrl":null,"url":null,"abstract":"In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting.We begin by describing possible formulations of the problem and offer commentary on related topics and challenges in obtaining globally optimal solutions. Key theorems are provided, and the paper closes with some expository examples.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":"53 2","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review\",\"authors\":\"Jonathan Gillard, Konstantin Usevich\",\"doi\":\"10.4310/22-sii735\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting.We begin by describing possible formulations of the problem and offer commentary on related topics and challenges in obtaining globally optimal solutions. Key theorems are provided, and the paper closes with some expository examples.\",\"PeriodicalId\":51230,\"journal\":{\"name\":\"Statistics and Its Interface\",\"volume\":\"53 2\",\"pages\":\"\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2023-04-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistics and Its Interface\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.4310/22-sii735\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics and Its Interface","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.4310/22-sii735","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

在本文中，我们提供了关于汉克尔低秩近似和完成的工作的回顾和参考书目，特别强调了如何将这种方法用于时间序列分析和预测。我们首先描述问题的可能形式，并对获得全局最优解的相关主题和挑战提供评论。给出了一些关键定理，最后给出了一些说明性的例子。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review

In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting.We begin by describing possible formulations of the problem and offer commentary on related topics and challenges in obtaining globally optimal solutions. Key theorems are provided, and the paper closes with some expository examples.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Statistics and Its Interface MATHEMATICAL & COMPUTATIONAL BIOLOGY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

0.90

自引率

12.50%

发文量

审稿时长

6 months

期刊介绍： Exploring the interface between the field of statistics and other disciplines, including but not limited to: biomedical sciences, geosciences, computer sciences, engineering, and social and behavioral sciences. Publishes high-quality articles in broad areas of statistical science, emphasizing substantive problems, sound statistical models and methods, clear and efficient computational algorithms, and insightful discussions of the motivating problems.