Markdown Optimization with Generalized Weighted Least Squares Estimation

Abstract

Retailers increasingly apply price markdowns for their seasonal products. Efficiency of these markdown applications is driven by the accuracy of empirical models, especially toward the end of a selling season. In the literature, recent sales are recognized to be more important than older sales data for estimating the current period’s demand for a given markdown level. The importance difference between the weeks of a selling season is addressed by weighted least squares (WLS) method with continuous weight functions of time. This study suggests a generalization of the weight functions and a method for optimizing their shape and discretization parameters to stimulate the predictive accuracy of models. We find that addressing the importance difference of recent sales observations using our generalized weight functions improves the forecast accuracy by up to 20%, and most of the improvement stems from our weight discretization method.

Evaluation of Various Machine Learning Methods to Predict Istanbul’s Freshwater Consumption

“…In contrast to the classical models, these models have more complex mathematical structures, and they can signal issues such as overfitting. Tuning these input parameters and finding the best-fit combination is another optimization problem and require more work time than classical models (Hekimoğlu, 2022). Detailed explanations about the applications of the models can be founded in the appendix.…”