  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.
