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

Gillard, Jonathan ORCID: 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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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: Mathematics
Publisher: Elsevier
ISSN: 0169-2070
Date of First Compliant Deposit: 20 June 2018
Date of Acceptance: 13 June 2018
Last Modified: 07 Nov 2023 23:24

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