Statistics and Its Interface
Volume 8 (2015)
Application of structured low-rank approximation methods for imputing missing values in time series
Pages: 321 – 330
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.
time series, missing data, Hankel structured low-rank approximation
Published 17 April 2015