Demand prediction is a crucial task for e-commerce and physical retail
businesses, especially during high-stake sales events. However, the limited
availability of historical data from these peak periods poses a significant
challenge for traditional forecasting methods. In this paper, we propose a
novel approach that leverages strategically chosen proxy data reflective of
potential sales patterns from similar entities during non-peak periods,
enriched by features learned from a graph neural networks (GNNs)-based
forecasting model, to predict demand during peak events. We formulate the
demand prediction as a meta-learning problem and develop the Feature-based
First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm that leverages
proxy data from non-peak periods and GNN-generated relational metadata to learn
feature-specific layer parameters, thereby adapting to demand forecasts for
peak events. Theoretically, we show that by considering domain similarities
through task-specific metadata, our model achieves improved generalization,
where the excess risk decreases as the number of training tasks increases.
Empirical evaluations on large-scale industrial datasets demonstrate the
superiority of our approach. Compared to existing state-of-the-art models, our
method demonstrates a notable improvement in demand prediction accuracy,
reducing the Mean Absolute Error by 26.24% on an internal vending machine
dataset and by 1.04% on the publicly accessible JD.com dataset.

我们提出了一种新颖的方法，利用非高峰时期的类似实体的代理数据以及基于图神经网络的特征学习模型，将需求预测问题转化为元学习问题，并开发了基于特征的第一阶段模型无关元学习算法 (F-FOMAML)，通过利用代理数据和 GNN 生成的关系元数据来学习特定特征层参数，以适应高峰活动的需求预测。理论上，我们证明了通过考虑任务特定元数据的领域相似性，我们的模型实现了改进的泛化性能，即随着训练任务数量的增加，冗余风险减少。对大规模工业数据集的实证评估表明了我们方法的优越性。与现有的最先进模型相比，我们的方法在需求预测准确性方面取得了显著的改进，在内部自动贩卖机数据集上的平均绝对误差减少了 26.24%，在公开可访问的京东数据集上减少了 1.04%。