Structured low-rank matrix completion for forecasting in time series analysis

Gillard, Jonathan ORCID: https://orcid.org/0000-0001-9166-298X and Usevich, Konstantin 2018. Structured low-rank matrix completion for forecasting in time series analysis. International Journal of Forecasting 34 (4) , pp. 582-597. 10.1016/j.ijforecast.2018.03.008

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Abstract

This paper considers the low-rank matrix completion problem, with a specific application to forecasting in time series analysis. Briefly, the low-rank matrix completion problem is the problem of imputing missing values of a matrix under a rank constraint. We consider a matrix completion problem for Hankel matrices and a convex relaxation based on the nuclear norm. Based on new theoretical results and a number of numerical and real examples, we investigate the cases in which the proposed approach can work. Our results highlight the importance of choosing a proper weighting scheme for the known observations.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Mathematics
Publisher:	Elsevier
ISSN:	0169-2070
Date of First Compliant Deposit:	20 June 2018
Date of Acceptance:	13 June 2018
Last Modified:	22 Nov 2024 21:30
URI:	https://orca.cardiff.ac.uk/id/eprint/112606

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