Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review
Numerical Analysis
2022-06-13 v1 Numerical Analysis
Methodology
Machine Learning
Abstract
In this paper we offer a review and bibliography of work on Hankel low-rank approximation and completion, with particular emphasis on how this methodology can be used for time series analysis and forecasting. We begin by describing possible formulations of the problem and offer commentary on related topics and challenges in obtaining globally optimal solutions. Key theorems are provided, and the paper closes with some expository examples.
Keywords
Cite
@article{arxiv.2206.05103,
title = {Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review},
author = {Jonathan Gillard and Konstantin Usevich},
journal= {arXiv preprint arXiv:2206.05103},
year = {2022}
}