文章基本信息

标题：Application of structured low-rank approximation methods for imputing missing values in time series
作者：Jonathan Gillard ; Anatoly Zhigljavsky
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2015
卷号：8
期号：3
页码：321-330
DOI：10.4310/SII.2015.v8.n3.a6
出版社：International Press
摘要：In this paper we consider an important statistical problem of imputing missing values into a time series data. We formulate this problem as a problem of structured low-rank approximation (SLRA), which is a problem of matrix analysis. One of the main difficulties in this SLRA problem is related to the fact that the norm which defines the quality of low-rank approximations is different from the Frobenius norm.We argue that the arising SLRA problem is a very difficult optimization problem and then consider and compare a number of algorithms for its solution.
关键词：time series; missing data; Hankel structured low-rank approximation