# Data-driven jet fuel demand forecasting: A case study of Copenhagen Airport

arXiv: https://arxiv.org/abs/2511.05569 · HTML: https://mlsanity.com/paper/2511.05569/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 4 Nov 2025.

## Builds on (most cited first, 5 of 5)

- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Stream-based active learning with linear models](https://mlsanity.com/paper/2207.09874/) (2022)
- [Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders](https://mlsanity.com/paper/2212.13067/) (2022)
- [Robust online active learning](https://mlsanity.com/paper/2302.00422/) (2023)
- [Flight Time Prediction for Fuel Loading Decisions with a Deep Learning Approach](https://mlsanity.com/paper/2005.05684/) (2020)

## Abstract (arXiv)

Accurate forecasting of jet fuel demand is crucial for optimizing supply chain operations in the aviation market. Fuel distributors specifically require precise estimates to avoid inventory shortages or excesses. However, there is a lack of studies that analyze the jet fuel demand forecasting problem using machine learning models. Instead, many industry practitioners rely on deterministic or expertise-based models. In this research, we evaluate the performance of data-driven approaches using a substantial amount of data obtained from a major aviation fuel distributor in the Danish market. Our analysis compares the predictive capabilities of traditional time series models, Prophet, LSTM sequence-to-sequence neural networks, and hybrid models. A key challenge in developing these models is the required forecasting horizon, as fuel demand needs to be predicted for the next 30 days to optimize sourcing strategies. To ensure the reliability of the data-driven approaches and provide valuable insights to practitioners, we analyze three different datasets. The primary objective of this study is to present a comprehensive case study on jet fuel demand forecasting, demonstrating the advantages of employing data-driven models and highlighting the impact of incorporating additional variables in the predictive models.