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.

翻译：需求预测是一项重要的商业应用，能够帮助零售商优化库存规划、物流及核心业务决策。需求预测的关键挑战之一在于考虑商品之间的关联和交互作用。大多数现代预测方法仅提供独立的单品级预测，未考虑相关商品的影响。近期研究尝试利用图神经网络应对这一挑战，并展现出良好的效果。本文在前人关于图神经网络的研究基础上做出两项贡献：首先，我们将图神经网络编码器集成到当前最先进的DeepAR模型中，该组合模型能够产生概率预测结果，这对于不确定性下的决策至关重要；其次，我们提出基于商品属性相似性构建图结构，从而避免对预定义图结构的依赖。在三个真实数据集上的实验表明，所提方法持续优于非图基线模型。此外，我们的方法能够生成商品嵌入表示，这些嵌入不仅编码了商品相似性与需求动态特征，还可用于预测之外的其它下游商业任务。