16,637 research outputs found
Structured low-rank matrix completion for forecasting in time series analysis
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.Comment: 25 pages, 12 figure
Robust Structured Low-Rank Approximation on the Grassmannian
Over the past years Robust PCA has been established as a standard tool for
reliable low-rank approximation of matrices in the presence of outliers.
Recently, the Robust PCA approach via nuclear norm minimization has been
extended to matrices with linear structures which appear in applications such
as system identification and data series analysis. At the same time it has been
shown how to control the rank of a structured approximation via matrix
factorization approaches. The drawbacks of these methods either lie in the lack
of robustness against outliers or in their static nature of repeated
batch-processing. We present a Robust Structured Low-Rank Approximation method
on the Grassmannian that on the one hand allows for fast re-initialization in
an online setting due to subspace identification with manifolds, and that is
robust against outliers due to a smooth approximation of the -norm cost
function on the other hand. The method is evaluated in online time series
forecasting tasks on simulated and real-world data
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