# Factor-augmented tree ensembles

arXiv: https://arxiv.org/abs/2111.14000 · HTML: https://mlsanity.com/paper/2111.14000/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 84 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Abstract (arXiv)

This manuscript proposes to extend the information set of time-series regression trees with latent stationary factors extracted via state-space methods. In doing so, this approach generalises time-series regression trees on two dimensions. First, it allows to handle predictors that exhibit measurement error, non-stationary trends, seasonality and/or irregularities such as missing observations. Second, it gives a transparent way for using domain-specific theory to inform time-series regression trees. Empirically, ensembles of these factor-augmented trees provide a reliable approach for macro-finance problems. This article highlights it focussing on the lead-lag effect between equity volatility and the business cycle in the United States.