Temporal Graph Neural Networks for Multi-Product Time Series Forecasting

Abstraction: Temporal GNNs modeling cross-SKU dependencies for retail sales forecasting

Key points:

• Traditional tools (ARIMA, exponential smoothing) treat each product in isolation, assume linear trends, and fail when dozens of products interact or when external signals (promotions, holidays, weather) must be incorporated
• GNNs learn a sparse influence graph from data: a spike in one SKU propagates to related SKUs via learned adjacency
• Architecture combines three layers: learned graph adjacency, graph convolutions to blend neighbor information, and temporal convolutions to capture evolving patterns
• Framed as a supply-chain scenario: daily sales forecasting for multiple retail products (SKUs) end-to-end
• Article unpacks both mechanics and math — graph adjacency learned from data, not hand-defined

Connections: Graph Neural Networks · Time Series Forecasting · Supply Chain Optimization

Source: https://pub.towardsai.net/temporal-graph-neural-networks-for-multi-product-time-series-forecasting-f4cc87f8354c