DP-CDM: A Dual-Phase Conditional Diffusion Model for Demand Forecasting in Digital Supply Chains

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.