Statistics and Its Interface

Volume 10 (2017)

Number 1

Simple nuclear norm based algorithms for imputing missing data and forecasting in time series

Pages: 19 – 25

DOI: https://dx.doi.org/10.4310/SII.2017.v10.n1.a2

Authors

Holly Butcher (School of Mathematics, Cardiff University, Cardiff, Wales, United Kingdom)

Jonathan Gillard (School of Mathematics, Cardiff University, Cardiff, Wales, United Kingdom)

Abstract

There has been much recent progress on the use of the nuclear norm for the so-called matrix completion problem (the problem of imputing missing values of a matrix). In this paper we investigate the use of the nuclear norm for modelling time series, with particular attention to imputing missing data and forecasting. We introduce a simple alternating projections type algorithm based on the nuclear norm for these tasks, and consider a number of practical examples.

Keywords

nuclear norm, time series analysis, structured low rank approximation

2010 Mathematics Subject Classification

Primary 62M10, 62M15. Secondary 62P99.

Full Text (PDF format)

Published 27 September 2016