XGBoostLSS -- An extension of XGBoost to probabilistic forecasting
This provides a flexible tool for probabilistic forecasting in domains like finance or healthcare, though it is an incremental extension of an existing method.
The authors tackled the limitation of XGBoost predicting only the conditional mean by extending it to model the entire conditional distribution, including location, scale, and shape, enabling probabilistic forecasting with derived prediction intervals and quantiles.
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.