Demand forecasting is a prominent business use case that allows retailers to
optimize inventory planning, logistics, and core business decisions. One of the
key challenges in demand forecasting is accounting for relationships and
interactions between articles. Most modern forecasting approaches provide
independent article-level predictions that do not consider the impact of
related articles. Recent research has attempted addressing this challenge using
Graph Neural Networks (GNNs) and showed promising results. This paper builds on
previous research on GNNs and makes two contributions. First, we integrate a
GNN encoder into a state-of-the-art DeepAR model. The combined model produces
probabilistic forecasts, which are crucial for decision-making under
uncertainty. Second, we propose to build graphs using article attribute
similarity, which avoids reliance on a pre-defined graph structure. Experiments
on three real-world datasets show that the proposed approach consistently
outperforms non-graph benchmarks. We also show that our approach produces
article embeddings that encode article similarity and demand dynamics and are
useful for other downstream business tasks beyond forecasting.

该研究论文在需求预测中通过整合图神经网络到先进的 DeepAR 模型，并利用文章属性相似性构建图形，提出了一种解决独立文章预测和相关文章交互的方法，实验结果表明该方法在真实数据集上始终优于非图形基准，同时产生了对下游业务任务有用的文章嵌入。