Identifying and Overcoming Transformation Bias in Forecasting Models
This addresses a practical issue for forecasters using common transformations, offering incremental improvements to reduce under-forecasting bias.
The paper tackled the systematic negative bias introduced by log and square root transformations in sales forecasting models, and demonstrated that proposed bias correction methods improve model performance by up to 50%, with Tweedie regression emerging as a strong alternative.
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.