Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7408087 | International Journal of Forecasting | 2018 | 16 Pages |
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.
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Authors
Jonathan Gillard, Konstantin Usevich,