New forecasting method balances accuracy and stability for retail demand

Researchers have developed a new method for large-scale retail demand forecasting that prioritizes forecast stability alongside accuracy. This approach introduces a training-time penalty on consecutive within-series movement, aiming to prevent abrupt changes in forecasts. When tested on M5 demand series, the stability-aware hybrid model demonstrated significant improvements in forecast stability scores compared to XGBoost, with minimal changes in root mean squared error. AI

IMPACT Introduces a novel accuracy-stability trade-off for operational retail forecasting, potentially improving planning cycles.

RANK_REASON Academic paper detailing a new methodology for forecasting. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

• Apple M5 chip
• XGBoost

• paper
• product

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