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Simple nuclear norm based algorithms for imputing missing data and forecasting in time series

Butcher, Holly Louise and Gillard, Jonathan William 2017. Simple nuclear norm based algorithms for imputing missing data and forecasting in time series. Statistics and Its Interface 10 (1) , pp. 19-25. 10.4310/SII.2017.v10.n1.a2

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Abstract

There has been much recent progress on the use of the nuclear norm for the so-called matrix completion problem (the problem of imputing missing values of a matrix). In this paper we investigate the use of the nuclear norm for modelling time series, with particular attention to imputing missing data and forecasting. We introduce a simple alternating projections type algorithm based on the nuclear norm for these tasks, and consider a number of practical examples.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Uncontrolled Keywords: nuclear norm, time series analysis, structured low rank approximation
Publisher: International Press
ISSN: 1938-7989
Date of First Compliant Deposit: 28 September 2016
Date of Acceptance: 28 September 2016
Last Modified: 20 Nov 2017 11:22
URI: http://orca.cf.ac.uk/id/eprint/94968

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