Summary of Inter-series Transformer: Attending to Products in Time Series Forecasting, by Rares Cristian et al.
Inter-Series Transformer: Attending to Products in Time Series Forecasting
by Rares Cristian, Pavithra Harsha, Clemente Ocejo, Georgia Perakis, Brian Quanz, Ioannis Spantidakis, Hamza Zerhouni
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel approach to supply chain demand forecasting using Transformer-based neural networks. By leveraging the capabilities of these architectures, researchers aim to improve forecasting accuracy on datasets that exhibit challenging characteristics like sparsity and cross-series effects, which are common in supply chain management. The proposed method draws from recent advancements in time series forecasting on benchmark datasets, showcasing promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Supply chain demand forecasting is a crucial task that helps businesses predict what products will be needed when. Recently, new techniques have been developed to improve forecasting accuracy, especially for big and complex data sets. This paper explores the use of these new techniques, called Transformer neural networks, to better predict future demands in supply chains. By doing so, it aims to help companies make more informed decisions about what products to stock and when. |
Keywords
» Artificial intelligence » Time series » Transformer
