# Identifying and Overcoming Transformation Bias in Forecasting Models

arXiv: https://arxiv.org/abs/2208.12264 · HTML: https://mlsanity.com/paper/2208.12264/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [Tweedie Gradient Boosting for Extremely Unbalanced Zero-inflated Data](https://mlsanity.com/paper/1811.10192/) (2018)

## Built on by (most cited first, 1 of 1)

- [TranSUN: A Preemptive Paradigm to Eradicate Retransformation Bias Intrinsically from Regression Models in Recommender Systems](https://mlsanity.com/paper/2505.13881/) (2025)

## Abstract (arXiv)

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.