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Log and square root transformations of target variable are routinely used inforecasting models to predict future sales. These transformations often lead tobetter performing models. However, they also introduce a systematic negativebias (under-forecasting). In this paper, we demonstrate the existence of thisbias, dive deep into its root cause and introduce two methods to correct forthe bias. We conclude that the proposed bias correction methods improve modelperformance (by up to 50%) and make a case for incorporating bias correction inmodeling workflow.  We also experiment with `Tweedie' family of cost functions which circumventsthe transformation bias issue by modeling directly on sales. We conclude thatTweedie regression gives the best performance so far when modeling on salesmaking it a strong alternative to working with a transformed target variable.

Identifying and Overcoming Transformation Bias in Forecasting Models