Course in AI-Based Aviation Demand Forecasting
Online course of 300 hours in AI-based aviation demand forecasting, with 6 modules covering air transport demand, data preparation, classical forecasting models, machine learning, evaluation and deployment, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Describe the factors that drive air transport demand.
Prepare and clean traffic data for modelling.
Apply classical time series models.
Train machine learning models for demand forecasting.
Evaluate forecast accuracy using appropriate metrics.
Deploy and monitor a forecasting model in production.
Develop a demand forecasting project for an aviation case.
Who it's for
- Air demand analysts who want to specialise in AI-based aviation demand forecasting.
- Aviation data scientists who want to specialise in AI-based aviation demand forecasting.
- Revenue management analysts who want to specialise in AI-based aviation demand forecasting.
- Airline network planners who want to specialise in AI-based aviation demand forecasting.
- Airport planning analysts who want to specialise in AI-based aviation demand forecasting.
- Machine learning engineers who want to specialise in AI-based aviation demand forecasting.
- Engineering or science graduates looking to move into the airline industry.
What you'll work with
- ICAO Doc 8991 (manual on air traffic forecasting)
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2016/679 (GDPR)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- Regulation (EEC) 95/93 (slot allocation)
- Python (pandas, scikit-learn)
- Prophet
- R (forecast)
- TensorFlow
- Power BI
6 modules
1.1Demand units: passengers, seats and cargo1.2Seasonality, day of the week and time of day1.3Booking curve and lead time1.4Passenger segments and elasticity1.5Effect of price and competition1.6Market data and available sources1.7Load factor and yield metrics1.8Impact of events and the economy1.9Relationship with fleet planning1.10Workshop: exploring a real series
2.1Internal sources: bookings, sales and operations2.2External sources: market, weather and events2.3Data quality and outliers2.4Handling gaps and cancellations2.5Aggregation by route, flight and point of sale2.6Calendar and holiday variables2.7Information leakage in time series2.8Temporal splits for validation2.9Automating the data pipeline2.10Laboratory: building the dataset
3.1Moving average and exponential smoothing3.2Models with multiple seasonality3.3ARIMA and SARIMAX with regressors3.4Trend and seasonality decomposition3.5Booking curve models3.6Prediction intervals3.7Residual diagnostics3.8Comparison with simple baselines3.9When a simple model is sufficient3.10Laboratory: forecasting a route
4.1Supervised formulation of the problem4.2Temporal feature engineering4.3Boosted tree models4.4Neural networks for time series4.5Global models versus per-route models4.6Regularisation and overfitting control4.7Temporal validation and model selection4.8Interpretability of predictions4.9Computational cost and updating4.10Laboratory: global multi-route model
5.1Error metrics and their business interpretation5.2Error by horizon and by segment5.3Fair comparison between models5.4Retraining and data drift5.5Monitoring in production5.6Integration with revenue management5.7Explaining results to the business5.8Documentation and reproducibility5.9Model governance and responsibilities5.10Laboratory: monitoring dashboard
6.1Defining the scope and horizon6.2Preparing the dataset6.3Baseline and candidate models6.4Temporal validation and selection6.5Error analysis and difficult cases6.6Translating into capacity and pricing decisions6.7Deployment and monitoring plan6.8Estimating the economic impact6.9Report and presentation6.10Final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within the intake, so it can be combined with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (pandas, scikit-learn), Prophet, R (forecast), TensorFlow and Power BI, in free, academic or evaluation versions. Testing is studied through real procedures for forecasting with historical traffic series and time-based cross-validation. Assessment is continuous by module, plus the final project.
Standards covered: ICAO Doc 8991, Regulation (EU) 2024/1689, Regulation (EU) 2016/679, ISO/IEC 42001, ISO/IEC 23894, Regulation (EEC) 95/93.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- No previous qualification is required.
- An internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The course totals 300 hours over 3 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You will work with Python (pandas, scikit-learn), Prophet, R (forecast), TensorFlow and Power BI. Free, academic or evaluation versions are used, and your tutor indicates the free alternative where one exists.
A SEIUM Institute certificate, awarded on passing the continuous assessment and the final project. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
No previous qualification is required. You need an internet connection, a computer capable of running the programme software, and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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