Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 123 references that are in the map. First posted on arXiv 10 Jun 2022.
Builds on
13 of 123 references.
References · 13
- 2013Robust Spectral Compressed Sensing via Structured Matrix Completioncited by 13
- 2016Top-N Recommender System via Matrix Completioncited by 7
- 2019A convex relaxation to compute the nearest structured rank deficient matrixcited by 7
- 2018Structured low-rank matrix completion for forecasting in time series analysiscited by 6
- 2016Low-rank Optimization with Convex Constraintscited by 5
- 2016A Fast Algorithm for Convolutional Structured Low-Rank Matrix Recoverycited by 5
- 2013Variable projection methods for approximate (greatest) common divisor computationscited by 2
- 2013Multivariate and 2D Extensions of Singular Spectrum Analysis with the Rssa Packagecited by 2
- 2013Exact Solutions in Structured Low-Rank Approximationcited by 1
- 2015Robust Structured Low-Rank Approximation on the Grassmanniancited by 1
- 2015Iterative algorithms for weighted and unweighted finite-rank time-series approximationscited by 1
- 2018Denoising and Completion of Structured Low-Rank Matrices via Iteratively Reweighted Least Squarescited by 1
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Citing papers per year: 2023: 1.
1 paper.