XGBoostLSS -- An extension of XGBoost to probabilistic forecasting
Abstract
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.
Keywords
Cite
@article{arxiv.1907.03178,
title = {XGBoostLSS -- An extension of XGBoost to probabilistic forecasting},
author = {Alexander März},
journal= {arXiv preprint arXiv:1907.03178},
year = {2019}
}
Comments
Bayesian Optimization; Distributional Modeling; Expectile Regression; GAMLSS; Probabilistic Forecast; Uncertainty Quantification; XGBoost