CatBoostLSS -- An extension of CatBoost to probabilistic forecasting

• URL: http://arxiv.org/abs/2001.02121v1
• Date: Sat, 4 Jan 2020 15:42:44 GMT
• Title: CatBoostLSS -- An extension of CatBoost to probabilistic forecasting
• Authors: Alexander M\"arz
• Abstract summary: We propose a new framework that predicts the entire conditional distribution of a univariable response variable. CatBoostLSS models all moments of a parametric distribution instead of the conditional mean only. We present both a simulation study and real-world examples that demonstrate the benefits of our approach.
• Score: 91.3755431537592
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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