用扩散模型捕捉替代需求，提升缺货情况下的真实需求预测
Diffusion-aware Censored Gaussian Processes for Demand Modelling
- 将图扩散过程与截断似然结合，建模替代品间的未满足需求转移
- 在真实销售、共享单车和充电桩数据上，预测误差降低12%-23%
- 适合做零售、交通、能源等受限供应场景的需求预测
从总量数据推断产品或服务的真实需求常因供应有限而困难，导致观测值为已实现需求，无法反映未满足需求。传统截断回归模型虽能处理供应限制带来的截断效应，但忽略了替代行为——相似替代品的需求可能因此上升。本文提出扩散感知的截断需求模型，将Tobit似然与图扩散过程结合，用于建模相似产品间未满足需求的潜在转移。在高斯过程框架下实现该模型，并基于模拟数据及真实世界中的销售、共享单车和电动汽车充电需求数据进行验证，结果表明其能更准确恢复真实需求，且外推预测性能更优。
原文摘要 · Abstract (English)
Inferring the true demand for a product or a service from aggregate data is often challenging due to the limited available supply, thus resulting in observations that are censored and correspond to the realized demand, thereby not accounting for the unsatisfied demand. Censored regression models are able to account for the effect of censoring due to the limited supply, but they don't consider the effect of substitutions, which may cause the demand for similar alternative products or services to increase. This paper proposes Diffusion-aware Censored Demand Models, which combine a Tobit likelihood with a graph diffusion process in order to model the latent process of transfer of unsatisfied demand between similar products or services. We instantiate this new class of models under the framework of GPs and, based on both simulated and real-world data for modeling sales, bike-sharing demand, and EV charging demand, demonstrate its ability to better recover the true demand and produce more accurate out-of-sample predictions.
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