2022/08/24 by Sushant More, More, Sushant · 1 citation
Computer Science · Decision Sciences · Mathematics · #Advanced Statistical Process Monitoring #Applications (stat.AP) #FOS: Computer and information sciences #Forecasting Techniques and Applications #Machine Learning (cs.LG) #Stock Market Forecasting Methods #cs.LG #stat.AP
paper · pdf · doi:10.48550/arxiv.2208.12264
KDD 2022 Workshop on Mining and Learning from Time Series -- Deep Forecasting: Models, Interpretability, and Applications (accepted as a poster)
arxiv created 2022/08/24 · openalex publication_date 2022/08/24 · arxiv updated 2022/08/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.