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

Jonathan Gillard and Konstantin Usevich

International Journal of Forecasting, 2018, vol. 34, issue 4, 582-597

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.

Keywords: Hankel matrices; Low-rank matrix completion; Forecasting; Nuclear norm (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:34:y:2018:i:4:p:582-597

DOI: 10.1016/j.ijforecast.2018.03.008

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