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

Abhinav Garg; Naman Shukla; Maarten Wormer

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arXiv · 2407.20192

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

Abhinav Garg·Naman Shukla·Maarten Wormer

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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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BibTeX RIS

Abhinav Garg, Naman Shukla, Maarten Wormer. 2024-08-13. Time series forecasting with high stakes: A field study of the air cargo industry. https://arxiv.org/abs/2407.20192

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