  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.
