Time series forecasting with high stakes: A field study of the air cargo industry

• URL: http://arxiv.org/abs/2407.20192v2
• Date: Tue, 13 Aug 2024 21:40:07 GMT
• Title: Time series forecasting with high stakes: A field study of the air cargo industry
• Authors: Abhinav Garg, Naman Shukla, Maarten Wormer,
• Abstract summary: This paper focuses on the development and implementation of machine learning models in decision-making for the air cargo industry. We leverage a mixture of experts framework, combining statistical and advanced deep learning models to provide reliable forecasts for cargo demand over a six-month horizon.
• Score: 3.8335551408225967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series forecasting in the air cargo industry presents unique challenges due to volatile market dynamics and the significant impact of accurate forecasts on generated revenue. This paper explores a comprehensive approach to demand forecasting at the origin-destination (O\&D) level, focusing on the development and implementation of machine learning models in decision-making for the air cargo industry. We leverage a mixture of experts framework, combining statistical and advanced deep learning models to provide reliable forecasts for cargo demand over a six-month horizon. The results demonstrate that our approach outperforms industry benchmarks, offering actionable insights for cargo capacity allocation and strategic decision-making in the air cargo industry. While this work is applied in the airline industry, the methodology is broadly applicable to any field where forecast-based decision-making in a volatile environment is crucial.

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