# XGBoostLSS -- An extension of XGBoost to probabilistic forecasting

arXiv: https://arxiv.org/abs/1907.03178 · HTML: https://mlsanity.com/paper/1907.03178/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [Mambular: A Sequential Model for Tabular Deep Learning](https://mlsanity.com/paper/2408.06291/) (2024)
- [M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyond](https://mlsanity.com/paper/2107.06675/) (2021)
- [ScoringBench: A Benchmark for Evaluating Tabular Foundation Models with Proper Scoring Rules](https://mlsanity.com/paper/2603.29928/) (2026)
- [Unmasking Trees for Tabular Data](https://mlsanity.com/paper/2407.05593/) (2024)
- [Online Distributional Regression](https://mlsanity.com/paper/2407.08750/) (2024)
- [TabResFlow: A Normalizing Spline Flow Model for Probabilistic Univariate Tabular Regression](https://mlsanity.com/paper/2508.17056/) (2025)
- [TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression](https://mlsanity.com/paper/2206.04140/) (2022)
- [Forecasting with Hyper-Trees](https://mlsanity.com/paper/2405.07836/) (2024)
- [From Point to probabilistic gradient boosting for claim frequency and severity prediction](https://mlsanity.com/paper/2412.14916/) (2024)
- [Multivariate Uncertainty Quantification with Tomographic Quantile Forests](https://mlsanity.com/paper/2512.16383/) (2025)
- [Tree-based Forecasting of Day-ahead Solar Power Generation from Granular Meteorological Features](https://mlsanity.com/paper/2312.00090/) (2023)
- [Parallel gradient boosting for flexible estimation of conditional distributions](https://mlsanity.com/paper/2607.13550/) (2026)

## Abstract (arXiv)

We propose a new framework of XGBoost that predicts the entire conditional distribution of a univariate response variable. In particular, XGBoostLSS models all moments of a parametric distribution (i.e., mean, location, scale and shape [LSS]) instead of the conditional mean only. Choosing from a wide range of continuous, discrete and mixed discrete-continuous distribution, modelling and predicting the entire conditional distribution greatly enhances the flexibility of XGBoost, as it allows to gain additional insight into the data generating process, as well as to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived. We present both a simulation study and real world examples that demonstrate the virtues of our approach.