Summary of Probabilistic Demand Forecasting with Graph Neural Networks, by Nikita Kozodoi et al.
Probabilistic Demand Forecasting with Graph Neural Networks
by Nikita Kozodoi, Elizaveta Zinovyeva, Simon Valentin, João Pereira, Rodrigo Agundez
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents advancements in demand forecasting for retailers, tackling the challenge of accounting for relationships between articles. By integrating Graph Neural Networks (GNNs) into a state-of-the-art DeepAR model, the proposed approach generates probabilistic forecasts essential for decision-making under uncertainty. Additionally, the paper proposes building graphs based on article attribute similarity, eliminating reliance on predefined structures. Experimental results on three real-world datasets demonstrate consistent outperformance over non-graph benchmarks, and showcase the utility of generated article embeddings in downstream business tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps retailers make better decisions by predicting what customers will want to buy. Right now, most forecasting methods don’t consider how different products are connected. This paper fixes that problem by combining a special kind of AI called Graph Neural Networks with another powerful tool called DeepAR. The result is more accurate predictions that can help businesses make informed choices. The researchers also came up with a new way to build these connections between products, which doesn’t require knowing beforehand how they’re related. They tested their approach on three real-world datasets and found it did better than other methods. |
