Demand forecasting course at UAM
About our
The UAM Demand Forecasting Course trains participants in the use of advanced data analysis, predictive modeling, and machine learning techniques applied to estimating service demand in the urban air mobility (UAM) sector. It focuses on applying methodologies to predict route, schedule, and capacity demand, using advanced statistics, traffic simulation, and Big Data analysis tools relevant to the development of UAM infrastructure. The course explores future scenarios considering factors such as urbanization, regulations, and technology adoption for strategic planning and operational optimization.
The program provides practical skills in the use of software tools for data analysis and visualization, predictive modeling, and risk analysis. It addresses the impact of different variables, such as prices, vehicle availability, and weather conditions, on service demand. This training prepares students for roles such as demand analysts, route planners, urban mobility consultants, and data specialists, strengthening employability in the UAM sector.
Demand forecasting course at UAM
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
649 $
Competencies and results
What you will learn
1. Demand Forecasting: Advanced Techniques and Strategies UAM
- Understand the fundamentals of demand forecasting and its strategic importance.
- Master quantitative forecasting methods, including time series and econometric models.
- Apply qualitative techniques, such as surveys and expert judgment, to complement forecasting.
- Use specialized software for demand modeling and analysis.
- Evaluate and optimize forecast accuracy using key metrics.
- Develop demand management strategies to align supply and demand.
- Analyze the impact of external factors, such as market trends and economic events, on demand.
- Implement contingency plans to mitigate the risks associated with demand uncertainty.
- Use demand forecasting for decision-making in areas such as inventory planning, production, and marketing.
2. Demand Forecasting Mastery at UAM: Methods and Optimization
- Understand the fundamental principles of demand forecasting in the context of Urban Air Mobility (UAM).
- Become familiar with quantitative and qualitative methods for demand forecasting.
- Evaluate and select relevant data sources for UAM demand forecasting.
- Apply data analysis techniques, including descriptive and inferential statistics.
- Use regression and time series models to predict future demand for UAM services.
- Explore the use of machine learning algorithms to improve the accuracy of predictions.
- Identify and mitigate biases and uncertainty in forecasting models.
- Optimize demand forecasting strategies considering factors such as location, time, and service type.
- Integrate demand forecasting with capacity planning and management.
- Analyze the impact of demand forecasting on the profitability and sustainability of UAM operations.
UAM fleet.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. In-depth Analysis of Demand at UAM: Prediction and Applications
- Master advanced techniques for demand forecasting in the aeronautics and aerospace engineering fields.
- Interpret and apply complex mathematical models for demand analysis, including time series models and forecasting methods based on historical data and market trends.
- Utilize specialized software tools for data analysis and demand scenario simulation.
- Understand the key factors influencing the demand for products and services in the aerospace industry, such as technological innovation, government regulations, and global economic conditions.
- Evaluate the impact of design, manufacturing, and maintenance decisions on the lifecycle demand of aircraft and their components.
- Develop effective demand management strategies, including production optimization, inventory management, and capacity planning.
- Apply artificial intelligence and machine learning techniques to improve the accuracy of forecasts of demand and optimize decision-making.
Analyze real-world case studies and practical examples of the application of demand analysis in the aerospace sector.
Evaluate the impact of sustainability and new technologies on future aviation demand.
Develop effective communication skills to present the results of demand analysis to different audiences, including engineers, managers, and decision-makers.
5. UAM Demand Forecasting: Methods, Analysis and Strategies
5. UAM Demand Forecasting: Methods, Analysis, and Strategies
- Understand the fundamentals of demand forecasting in the context of Urban Air Mobility (UAM).
- Explore and apply various demand forecasting methods, including quantitative and qualitative techniques.
- Analyze historical and current data to identify patterns and trends in the demand for UAM services.
- Evaluate the impact of external factors, such as infrastructure, regulation, and market conditions, on demand.
- Develop strategies for demand management, including price optimization, capacity management, and service customization.
- Utilize software tools and data analysis platforms for UAM demand forecasting and simulation.
- Study case studies and practical examples of the application of demand forecasting in the UAM sector.
- Analyze and optimize business models for the sustainability of UAM demand.
Learn to adapt demand forecasting strategies to different scenarios and UAM business models.
Master the presentation of results and effective communication of demand forecasts to stakeholders.
6. Demand Forecasting UAM: Fundamentals, Models and Applications
6. **UAM Demand Forecasting: Fundamentals, Models, and Applications**
- Understand the fundamentals of demand forecasting in the context of Urban Air Mobility (UAM).
- Explore and analyze different demand forecasting models applicable to UAM, including models based on historical data, regression models, and time series models.
- Identify and evaluate the key factors that influence the demand for UAM services, such as geographic location, demographics, economic conditions, and regulations.
- Apply data analysis techniques to collect, clean, transform, and analyze data relevant to UAM demand forecasting.
- Use specialized software and tools to implement and evaluate selected demand forecasting models.
- Interpret the results of the forecasting models and generate reports that enable informed decision-making regarding the planning and management of UAM services.
- Analyze the impact of variability and the uncertainty in UAM demand forecasting.
Study case studies of real-world applications of demand forecasting in UAM.
Learn about the economic and operational implications of demand forecasting in the UAM sector.
Explore future trends and challenges in demand forecasting for UAM, including real-time data integration, scenario modeling, and adaptation to new technologies.
Who this program is for:
Demand forecasting course at UAM
- Aerospace, mechanical, industrial, automation engineers, or related disciplines.
- UAM/eVTOL industry professionals, including manufacturers (OEMs), maintenance, repair, and overhaul (MRO) companies, consulting firms, and technology research centers.
- Experts in flight testing, certification, avionics, control, and flight dynamics seeking to deepen their knowledge.
- Personnel from regulatory bodies, aeronautical authorities, and professionals involved in UAM/eVTOL development who require knowledge of regulatory compliance.
Recommended qualifications: Prior knowledge of aerodynamics, control systems, and structures is recommended. The course is taught in Spanish/English (B2+/C1). We offer bridging tracks to address any gaps in your knowledge.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Module 1 — Fundamentals of Demand Forecasting at UAM
1.1 Introduction to demand forecasting: key concepts and their importance at UAM
1.2 Collection and analysis of historical demand data
1.3 Identification of key factors influencing demand
1.4 Basic forecasting techniques: moving averages and exponential smoothing
1.5 Evaluation and selection of demand forecasting models
1.6 Time series analysis: components and patterns
1.7 Practical applications of demand forecasting at UAM
1.8 Interpretation of results and uncertainty management
1.9 Introduction to software tools for demand forecasting
1.10 Case studies: examples of demand forecasting in the UAM sector
2.2 Key Concepts of Demand Forecasting
2.2 Importance of Demand Forecasting in Business Environments
2.3 Types of Demand and Their Characteristics
2.4 Data Sources for Demand Forecasting
2.5 Introduction to Qualitative Forecasting Methods
2.6 Introduction to Quantitative Forecasting Methods
2.7 The Demand Forecasting Process
2.8 Demand Forecasting Tools and Software
2.9 Common Challenges in Demand Forecasting
2.20 Applications of Demand Forecasting in Different Industries
3.3 Fundamentals of Demand Forecasting
3.2 Time Series Models for Forecasting
3.3 Regression and Forecasting Models
3.4 Data Analysis for Demand Forecasting
3.5 Advanced Forecasting Techniques
3.6 Strategic Applications of Forecasting
3.7 Model Evaluation and Validation
3.8 Optimizing Demand Forecasting
4.4 Fundamentals of Demand Forecasting: Introduction to Analysis
4.2 Data Collection and Cleaning: Preparation for Analysis at UAM
4.3 Quantitative Forecasting Methods: Time Series
4.4 Quantitative Forecasting Methods: Regression and Econometric Models
4.5 Qualitative Forecasting Methods: Surveys and Expert Judgment
4.6 Model Evaluation and Selection: Accuracy Metrics
4.7 Demand Analysis: Influencing Factors and Patterns
4.8 Strategic Applications: Demand Forecasting in Planning
4.9 Inventory Optimization and Supply Chain Management
4.40 Case Studies: Real-World Applications and Challenges
5.5 Forecasting Methods: Introduction and Selection
5.5 Time Series: Decomposition and Adjustment
5.3 Regression Models: Predictor Variables and Analysis
5.4 ARIMA Models: Identification and Estimation
5.5 Exponential Smoothing Methods: Advanced Techniques
5.6 Demand Data: Cleaning and Transformation
5.7 Model Evaluation: Accuracy Metrics
5.8 Optimization Strategies: Fine-Tuning and Selection
5.9 Strategic Applications: Planning and Decision Making
5.50 Case Study: Implementation and Results Analysis
6.6 Fundamentals of Forecasting: Key Concepts and Theoretical Framework
6.2 Time Series Models: Classical and Modern Techniques
6.3 Regression Models: Applications and Variable Analysis
6.4 Machine Learning Models: Introduction and Selection
6.5 Model Evaluation: Performance Metrics and Validation
6.6 Industry Applications: Real-World Case Studies
6.7 Data Analysis: Preparation and Cleaning
6.8 Software and Tools: Practical Implementation
6.9 Implementation Strategies: Integration into Decision-Making
6.60 The Future of Forecasting: Trends and Technological Advances
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).