<![CDATA[Accurate retail demand forecasting is frequently impeded by high-dimensional hierarchies and intermittent sales patterns, which destabilize traditional models and compromise operational decision-making. To address these challenges, this study develops a stability-aware forecasting framework that unifies global machine learning ensembles with hierarchical reconciliation and conformal uncertainty calibration. Utilizing the large-scale M5 dataset, the methodology synergizes decomposition-based feature engineering with a global Light Gradient Boosting Machine (LightGBM), reinforced by a robust Bottom-Up reconciliation strategy and Centered Conformalized Quantile Regression (CQR). Empirical results based on rolling-origin cross-validation demonstrate that the proposed framework achieves a superior Weighted Root Mean Squared Scaled Error (WRMSSE) of 8.7723, significantly outperforming both the standalone LightGBM (9.4846) and the Seasonal Naïve baseline (10.1740). Furthermore, the Centered CQR mechanism effectively balances predictive sharpness with coverage, attaining a Scaled Pinball Loss (SPL) of 0.2347, thereby mitigating error degradation often observed in sparse data regimes. These findings confirm that integrating structural decomposition with rigorous reconciliation acts as a potent regularizer, offering a scientifically robust solution for managing non-stationarity and signal sparsity in complex retail supply chains.]]>
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