# High dimensional VAR with low rank transition

arXiv: https://arxiv.org/abs/1905.00959 · HTML: https://mlsanity.com/paper/1905.00959/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2019.

## Built on by (most cited first, 4 of 4)

- [Time Series Analysis and Modeling to Forecast: a Survey](https://mlsanity.com/paper/2104.00164/) (2021)
- [Long-Context Linear System Identification](https://mlsanity.com/paper/2410.05690/) (2024)
- [Low Rank Forecasting](https://mlsanity.com/paper/2101.12414/) (2021)
- [Deviation inequalities for stochastic approximation by averaging](https://mlsanity.com/paper/2102.08685/) (2021)

## Abstract (arXiv)

We propose a vector auto-regressive (VAR) model with a low-rank constraint on the transition matrix. This new model is well suited to predict high-dimensional series that are highly correlated, or that are driven by a small number of hidden factors. We study estimation, prediction, and rank selection for this model in a very general setting. Our method shows excellent performances on a wide variety of simulated datasets. On macro-economic data from Giannone et al. (2015), our method is competitive with state-of-the-art methods in small dimension, and even improves on them in high dimension.