# Random autoregressive models: A structured overview

arXiv: https://arxiv.org/abs/2009.08165 · HTML: https://mlsanity.com/paper/2009.08165/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Hebbian learning inspired estimation of the linear regression parameters from queries](https://mlsanity.com/paper/2311.03483/) (2023)
- [AI-Generated Image Recognition via Fusion of CNNs and Vision Transformers](https://mlsanity.com/paper/2606.27637/) (2026)

## Abstract (arXiv)

Models characterized by autoregressive structure and random coefficients are powerful tools for the analysis of high-frequency, high-dimensional and volatile time series. The available literature on such models is broad, but also sectorial, overlapping, and confusing. Most models focus on one property of the data, while much can be gained by combining the strength of various models and their sources of heterogeneity. We present a structured overview of the literature on autoregressive models with random coefficients. We describe hierarchy and analogies among models, and for each we systematically list properties, estimation methods, tests, software packages and typical applications.