Accuracy-Preserving Stability Regularization for Large-Scale Retail Demand Forecasting
Summary
This paper introduces a training-time stability regularization penalty to improve forecast stability without sacrificing accuracy, evaluated on M5 retail demand data, showing improvements in Forecast Stability Score while maintaining RMSE within 0.72%.
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# Accuracy-Preserving Stability Regularization for Large-Scale Retail Demand Forecasting Source: [https://arxiv.org/abs/2607.13331](https://arxiv.org/abs/2607.13331) [View PDF](https://arxiv.org/pdf/2607.13331) > Abstract:Retail demand forecasts are reused across replenishment, capacity, labor, and transportation planning cycles\. Point\-error objectives do not constrain abrupt movement between adjacent forecasts, while post\-hoc smoothing acts only after model fitting\. We ask whether a training\-time penalty on consecutive within\-series movement can improve horizontal forecast\-path stability without materially changing point accuracy\. The penalty is evaluated in a temporal\-structured pipeline combining recent\-demand embeddings with calendar, price, hierarchy, item, and store features\. On selected M5 demand series at 1000, 3000, and 4000\-series scales, the stability\-aware hybrid model improves Forecast Stability Score over XGBoost by 6\.91%, 6\.66%, and 7\.68%, respectively, while RMSE changes remain within 0\.72% across three random seeds\. Post\-hoc exponential smoothing attains lower raw movement but incurs a larger RMSE cost; training\-time regularization preserves more point accuracy and performs favorably under normalized stability\. These findings extend forecast evaluation from point\-error minimization toward an accuracy\-stability trade\-off perspective for operational retail forecasting\. ## Submission history From: Jize Li \[[view email](https://arxiv.org/show-email/3ddf02f8/2607.13331)\] **\[v1\]**Tue, 14 Jul 2026 23:31:00 UTC \(905 KB\)
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