# Multivariate Boosted Trees and Applications to Forecasting and Control

arXiv: https://arxiv.org/abs/2003.03835 · HTML: https://mlsanity.com/paper/2003.03835/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2020.

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

- [Multi-Target XGBoostLSS Regression](https://mlsanity.com/paper/2210.06831/) (2022)
- [Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge](https://mlsanity.com/paper/2306.06142/) (2023)

## Abstract (arXiv)

Gradient boosted trees are competition-winning, general-purpose, non-parametric regressors, which exploit sequential model fitting and gradient descent to minimize a specific loss function. The most popular implementations are tailored to univariate regression and classification tasks, precluding the possibility of capturing multivariate target cross-correlations and applying structured penalties to the predictions. In this paper, we present a computationally efficient algorithm for fitting multivariate boosted trees. We show that multivariate trees can outperform their univariate counterpart when the predictions are correlated. Furthermore, the algorithm allows to arbitrarily regularize the predictions, so that properties like smoothness, consistency and functional relations can be enforced. We present applications and numerical results related to forecasting and control.