Probabilistic Demand Forecasting with Graph Neural Networks
Nikita Kozodoi Elizaveta Zinovyeva Simon Valentin João Pereira Rodrigo Agundez
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.