| By: |
Yue Qiu (Finance School, Shanghai University of International Business and Economics, Shanghai, China);
Wenbin Wang (Finance School, Shanghai University of International Business and Economics, Shanghai, China);
Tian Xie (College of Business, Shanghai University of Finance and Economics, Shanghai, China);
Jun Yu (Faculty of Business Administration, University of Macau, Macao);
Xinyu Zhang (Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China) |
| Abstract: |
Many real-world analytics problems, such as forecasting sales of fashion
products, involve uncertain and heterogeneous demand, requiring prescriptive
analytics to incorporate multiple covariates and address the inherent
challenge of model uncertainty. Traditional predict-thenoptimize (PTO)
approaches typically rely on a single predictive model, overlooking model
uncertainty. To address this, we propose an ensemble learning framework that
integrates Mallows-type model averaging into the PTO paradigm, leveraging
diverse candidate models with varying covariates to enhance forecast accuracy
and decision robustness. Theoretically, we prove that the weighted forecasts
achieve asymptotic optimality under mild conditions and establish
finite-sample risk bounds, ensuring stable performance even in limited-data
settings. We empirically evaluate the proposed framework using weekly
store-level sales data from an internationally recognized footwear brand in
China. The forecasting exercise demonstrates that our approach consistently
achieves the lowest prediction risk, improving forecast accuracy by 4.72% to
7.41% compared to the best-performing alternatives without weighted forecast
features. In the subsequent decision optimization exercise, we identify gift,
combo, and discount promotions as key decision variables and show that our
framework delivers the highest predicted sales responses on average,
outperforming alternative forecasting methods and existing data-driven
decision frameworks. |
| Keywords: |
data-driven, model uncertainty, model averaging, prescriptive analytics, machine learning, fashion sales forecasting |
| Date: |
2025–03 |
| URL: |
https://d.repec.org/n?u=RePEc:boa:wpaper:202525 |