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A Commodity Demand Forecasting System Based on Dual-Phase Conditional Diffusion Model

Fudong Wang et al.

International Journal of Data Warehousing and Mining2026https://doi.org/10.4018/ijdwm.401351article
ABDC C
Weight
0.50

What the paper says

Accurate demand forecasting enables efficient supply chain management. However, two major challenges persist: (a) insufficient modeling of external conditional variables and poor capture of complex multimodal demand distributions and (b) conditional information that is fused only once at the system input, causing information decay and reduced responsiveness to event-driven shocks. To this end, the authors propose a dual-phase conditional diffusion model (DP-CDM) in which a reverse sliding diffusion along the temporal axis exploits temporal continuity to build an autoregressive mechanism, enhancing sequence modeling and avoiding structural misalignment. In addition, noise-degradation diffusion enriches multimodal probabilistic representations and improves robustness to external disturbances. A conditional embedding module aligns multimodal features by aggregating local histories, global trends, and SHapley Additive exPlanations (SHAP)-quantified external factors, which are injected throughout the denoising processes. A 3.7% improvement in fitting performance showed the effectiveness of this model in capturing event-driven demand dynamics.

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https://doi.org/https://doi.org/10.4018/ijdwm.401351

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@article{fudong2026,
  title        = {{A Commodity Demand Forecasting System Based on Dual-Phase Conditional Diffusion Model}},
  author       = {Fudong Wang et al.},
  journal      = {International Journal of Data Warehousing and Mining},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijdwm.401351},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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