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

Jonathan Gillard; Konstantin Usevich

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arXiv · 1802.08242

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

Jonathan Gillard·Konstantin Usevich

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Abstract

In this paper we consider the low-rank matrix completion problem with 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 when the proposed approach can work. Our results highlight the importance of choosing a proper weighting scheme for the known observations.

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Jonathan Gillard, Konstantin Usevich. 2018-02-22. Structured low-rank matrix completion for forecasting in time series analysis. https://arxiv.org/abs/1802.08242

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