ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Structured Low-Rank Matrix Completion for Forecasting in Time Series Analysis

4 September 2018, 12:20 – 12:40

Abstract

Submitted by
Konstantin Usevich
Authors
Konstantin Usevich (Université de Lorraine, CNRS, CRAN), Jonathan Gillard (Cardiff University)
Abstract
In this talk 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. This work was supported in part by ERC AdG-2013-320594 DECODA.

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