Summary of Time Series Forecasting with High Stakes: a Field Study Of the Air Cargo Industry, by Abhinav Garg et al.
Time series forecasting with high stakes: A field study of the air cargo industry
by Abhinav Garg, Naman Shukla, Maarten Wormer
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 The paper presents a comprehensive approach to demand forecasting at the origin-destination (O&D) level for the air cargo industry. It develops and implements machine learning models for decision-making, leveraging a mixture of experts framework combining statistical and deep learning models. The approach provides reliable forecasts for cargo demand over a six-month horizon, outperforming industry benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps predict how much cargo will be moved between specific places, which is important because accurate predictions can help the air cargo industry make good decisions about how to use its resources. The researchers used a special type of artificial intelligence called machine learning to create models that are better at predicting cargo demand than what was being used before. This new approach can help the air cargo industry make more money by making better decisions. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Mixture of experts
