New framework improves retail demand forecasting with adaptive correction

Researchers have developed a new framework called Predict-then-Correct (PtC) to improve retail demand forecasting, particularly for situations with rapidly changing demand and limited early data. This framework combines an initial machine learning forecast with a few-shot continuous contextual bandit correction policy. Tested on Walmart retail data and a beverage dataset, PtC demonstrated significant reductions in forecasting errors (MAPE, MAE, RMSE) compared to existing methods and reduced inventory costs. AI

IMPACT This framework could lead to more accurate inventory management and reduced costs in retail by adapting to real-time demand shifts.

RANK_REASON The cluster contains a research paper detailing a new machine learning framework for demand forecasting. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Mae
• Mape Morottaja
• Proximal Policy Optimization
• Soft Actor--Critic
• Walmart

• paper
• product

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