# Structured low-rank matrix completion for forecasting in time series analysis

arXiv: https://arxiv.org/abs/1802.08242 · HTML: https://mlsanity.com/paper/1802.08242/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 6 of 6)

- [Accelerating Ill-conditioned Hankel Matrix Recovery via Structured Newton-like Descent](https://mlsanity.com/paper/2406.07409/) (2024)
- [Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theory](https://mlsanity.com/paper/2501.15388/) (2025)
- [Low Rank Forecasting](https://mlsanity.com/paper/2101.12414/) (2021)
- [Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review](https://mlsanity.com/paper/2206.05103/) (2022)
- [Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series](https://mlsanity.com/paper/2205.15548/) (2022)
- [Exact Recovery of Non-Random Missing Multidimensional Time Series via Temporal Isometric Delay-Embedding Transform](https://mlsanity.com/paper/2512.10191/) (2025)

## Abstract (arXiv)

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.