# CatBoostLSS -- An extension of CatBoost to probabilistic forecasting

arXiv: https://arxiv.org/abs/2001.02121 · HTML: https://mlsanity.com/paper/2001.02121/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression](https://mlsanity.com/paper/2206.04140/) (2022)
- [From Point to probabilistic gradient boosting for claim frequency and severity prediction](https://mlsanity.com/paper/2412.14916/) (2024)
- [Multivariate quantile regression via Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2609.23906/) (2026)

## Abstract (arXiv)

We propose a new framework of CatBoost that predicts the entire conditional distribution of a univariate response variable. In particular, CatBoostLSS 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 distributions, modelling and predicting the entire conditional distribution greatly enhances the flexibility of CatBoost, as it allows to gain 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 benefits of our approach.