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We propose a new framework of CatBoost that predicts the entire conditionaldistribution of a univariate response variable. In particular, CatBoostLSSmodels all moments of a parametric distribution (i.e., mean, location, scaleand shape [LSS]) instead of the conditional mean only. Choosing from a widerange of continuous, discrete and mixed discrete-continuous distributions,modelling and predicting the entire conditional distribution greatly enhancesthe flexibility of CatBoost, as it allows to gain insight into the datagenerating process, as well as to create probabilistic forecasts from whichprediction intervals and quantiles of interest can be derived. We present botha simulation study and real-world examples that demonstrate the benefits of ourapproach.

CatBoostLSS -- An extension of CatBoost to probabilistic forecasting