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

arXiv 2407.20192 ↗First posted 29 Jul 2024cs.LG cs.SY eess.SYcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 39 references that are in the map. First posted on arXiv 29 Jul 2024.

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References · 6

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  4. 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
  5. 2019Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019cited by 270
  6. 2015The Optimised Theta Methodcited by 1

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