论文标题
在预测模型中识别和克服转化偏差
Identifying and Overcoming Transformation Bias in Forecasting Models
论文作者
论文摘要
目标变量的日志和平方根变换通常用于预测模型中，以预测未来的销售。这些转换通常会导致更好的性能模型。但是，他们还引入了系统的负面偏见（遗产不足）。在本文中，我们证明了这种偏见的存在，深入研究了其根本原因，并引入了两种校正偏见的方法。我们得出的结论是，提出的偏差校正方法提高了模型性能（最多可提高50％），并为将偏差校正纳入建模工作流程而提高。 我们还尝试了“ Tweedie”成本功能家族的家族，这些功能通过直接在销售上进行建模来规避转型偏差问题。我们得出的结论是，Tweedie回归迄今为止在销售建模时提供了最佳性能，这是与转换的目标变量合作的强大替代方案。
Log and square root transformations of target variable are routinely used in forecasting models to predict future sales. These transformations often lead to better performing models. However, they also introduce a systematic negative bias (under-forecasting). In this paper, we demonstrate the existence of this bias, dive deep into its root cause and introduce two methods to correct for the bias. We conclude that the proposed bias correction methods improve model performance (by up to 50%) and make a case for incorporating bias correction in modeling workflow. We also experiment with `Tweedie' family of cost functions which circumvents the transformation bias issue by modeling directly on sales. We conclude that Tweedie regression gives the best performance so far when modeling on sales making it a strong alternative to working with a transformed target variable.
