论文标题
catboostlss- catboost到概率预测的扩展
CatBoostLSS -- An extension of CatBoost to probabilistic forecasting
论文作者
论文摘要
我们提出了一个新的catboost框架，该框架可以预测单变量响应变量的整个条件分布。尤其是，Catboostlss模拟参数分布的所有矩（即平均，位置，比例和形状[LSS]），而不是仅条件均值。从各种连续，离散和混合离散连续分布中进行选择，对整个条件分布进行建模和预测，从而大大提高了Catboost的灵活性，因为它允许深入了解数据生成过程，并创建概率的预测，并从中可以得出利益的预测间隔和分量。我们提出了一项模拟研究和现实世界的示例，以证明我们方法的好处。
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.
