Event-preserving feature engineering for intermittent demand forecasting using SHOS

Intermittent demand forecasting remains a persistent challenge in large-scale supply chains due to extreme sparsity, irregular demand occurrence, and highly variable demand magnitudes. While recent studies have increasingly emphasized architectural complexity to address these challenges, comparatively less attention has been directed toward the role of statistically grounded feature representation under sparse-demand conditions. This study introduces the Smoothed Hybrid Occurrence-Size (SHOS) framework as an event-preserving feature-engineering approach for intermittent-demand forecasting. SHOS decomposes demand into latent occurrence probability and conditional demand size components and generates adaptive, series-specific statistical representations using sparsity-aware exponential smoothing. Rather than functioning as a standalone forecasting model, SHOS operates as a feature-generation mechanism that embeds occurrence-size structure directly into supervised learning pipelines. The proposed framework was evaluated using a large-scale automotive aftermarket dataset consisting of approximately 56,000 dealer-part time series and approximately 1.4 million monthly observations generated after preprocessing and zero-padding. Models were evaluated using rolling-window cross-validation, signal-preservation analysis, and robustness studies across heterogeneous demand segments. Under the evaluated sparse-demand setting and model configurations, SHOS-augmented tree-based models substantially improved forecasting accuracy and stability relative to raw-feature baselines. In particular, the SHOS-augmented LightGBM configuration reduced the mean absolute error by approximately 46.3% relative to the baseline model, while the weighted mean absolute percentage error improved by more than 40%. Beyond conventional forecasting accuracy, the study additionally demonstrates that SHOS preserves event timing, peak structure, and frequency-domain characteristics that are often distorted by conventional smoothing approaches. The results further suggest that statistically grounded feature representations can substantially improve sparse-demand learning behaviour under the evaluated automotive aftermarket forecasting environment. Rather than establishing a universal hierarchy between representation and architectural complexity, the findings demonstrate the effectiveness of event-preserving representation-oriented forecasting under highly intermittent demand conditions.