高风险场景下的时间序列预测：航空货运行业的实地研究
Time series forecasting with high stakes: A field study of the air cargo industry
AI总结：
针对航空货运市场波动下需求预测影响营收的问题，提出结合统计与深度学习的混合专家框架，实现六个月货运需求预测，效果优于行业基准，方法可推广至多波动环境决策场景。
摘要：
航空货运行业的时间序列预测因市场动态波动以及预测准确性对营收的显著影响而面临独特挑战。本文探索了一种在起讫点（O&D）层面进行需求预测的综合方法，重点研究机器学习模型在航空货运行业决策中的开发与应用。我们采用混合专家（mixture of experts）框架，结合统计模型与先进深度学习模型，为未来六个月的货运需求提供可靠预测。结果表明，该方法优于行业基准，可为航空货运行业的运力分配与战略决策提供可落地的洞见。尽管本研究应用于航空业，但其方法论可广泛适用于任何在波动环境中基于预测进行决策至关重要的领域。
英文摘要：
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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