DOI: 10.57760/sciencedb.28583
title: DP-CDM: A Dual-Phase Conditional Diffusion Model for Demand Forecasting in Digital Supply Chains
title: DP-CDM: A Dual-Phase Conditional Diffusion Model for Demand Forecasting in Digital Supply Chains

description: Accurate demand forecasting is vital for digital supply chains, enabling efficient inventory, planning, and logistics. Existing models suffer from two key limitations: (i) they fail to adequately model the influence of external conditional variables, and exhibit limited ability to capture complex multi-modal distributions inherent in real-world demand data; and (ii) conditional information is concatenated with historical inputs only once at the model entrance, which often leads to information fading during deep propagation and reduces sensitivity to event-driven demand shocks. To address these challenges, we propose DP-CDM, a dual-phase conditional diffusion model for demand forecasting. First phase, a reverse sliding diffusion is applied along the temporal axis, which exploits temporal continuity to construct an autoregressive learning mechanism, thereby strengthening sequence modeling and avoiding structural misalignment. Second phase, a noise-degradation diffusion enriches multimodal probabilistic representations while improving robustness against exogenous disturbances. Moreover, we design a conditional embedding module with multi-modal feature alignment, which aggregates local historical windows, global trends, and SHAP-quantified external factors into multimodal embeddings. They are injected consistently throughout the dual denoising process to guide the final forecasts. Extensive experiments demonstrate DP-CDM reduces MAPE by 1.5 percentage points and improves R² by 4.4%, highlighting it effectiveness in capturing event-driven dynamics.
description: Accurate demand forecasting is vital for digital supply chains, enabling efficient inventory, planning, and logistics. Existing models suffer from two key limitations: (i) they fail to adequately model the influence of external conditional variables, and exhibit limited ability to capture complex multi-modal distributions inherent in real-world demand data; and (ii) conditional information is concatenated with historical inputs only once at the model entrance, which often leads to information fading during deep propagation and reduces sensitivity to event-driven demand shocks. To address these challenges, we propose DP-CDM, a dual-phase conditional diffusion model for demand forecasting. First phase, a reverse sliding diffusion is applied along the temporal axis, which exploits temporal continuity to construct an autoregressive learning mechanism, thereby strengthening sequence modeling and avoiding structural misalignment. Second phase, a noise-degradation diffusion enriches multimodal probabilistic representations while improving robustness against exogenous disturbances. Moreover, we design a conditional embedding module with multi-modal feature alignment, which aggregates local historical windows, global trends, and SHAP-quantified external factors into multimodal embeddings. They are injected consistently throughout the dual denoising process to guide the final forecasts. Extensive experiments demonstrate DP-CDM reduces MAPE by 1.5 percentage points and improves R² by 4.4%, highlighting it effectiveness in capturing event-driven dynamics.