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

Butcher, Holly and Gillard, Jonathan ORCID: https://orcid.org/0000-0001-9166-298X 2017. Simple nuclear norm based algorithms for imputing missing data and forecasting in time series. Statistics and Its Interface 10 (1) , pp. 19-25. 10.4310/SII.2017.v10.n1.a2

Preview

PDF - Accepted Post-Print Version Download (288kB) | Preview

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics
Uncontrolled Keywords:	nuclear norm, time series analysis, structured low rank approximation
Publisher:	International Press
ISSN:	1938-7989
Date of First Compliant Deposit:	28 September 2016
Date of Acceptance:	28 September 2016
Last Modified:	02 Dec 2024 05:00
URI:	https://orca.cardiff.ac.uk/id/eprint/94968

Citation Data

Cited 6 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item