The Machine Learning for Demand Forecasting course in Geneva is a specialized training course aimed at helping professionals utilize machine learning to improve demand prediction accuracy.
From:
13-07-2026
Course Overview
Accurate demand forecasting is vital for supply chain efficiency, inventory optimization, and strategic planning. This Machine Learning for Demand Forecasting Training Course introduces participants to modern ML techniques that outperform traditional forecasting methods.
Participants will learn how to build and evaluate forecasting models, use time-series analysis, and apply supervised and unsupervised learning approaches. Real-world case studies and practical labs will show how organizations leverage ML to anticipate demand, reduce costs, and improve decision-making.
By the end of the course, attendees will be able to design and implement machine learning models that deliver more reliable demand forecasts and support agile business strategies.
Course Benefits
Improve demand forecasting accuracy with ML
Apply predictive analytics for smarter planning
Optimize supply chain and inventory management
Anticipate customer demand and market fluctuations
Strengthen decision-making with AI-driven insights
Course Objectives
Explore machine learning applications in demand forecasting
Build time-series and regression-based forecasting models
Apply supervised and unsupervised ML techniques
Evaluate and validate model performance
Use ML for supply chain and sales demand predictions
Address data quality and feature engineering challenges
Integrate ML forecasting into business planning systems
Training Methodology
The course combines lectures, case studies, and hands-on labs with forecasting datasets. Participants will build and test ML models using real-world scenarios and platforms.
Target Audience
Supply chain and operations managers
Data scientists and analysts
Business strategists and planners
Professionals in retail, manufacturing, and logistics
Target Competencies
Machine learning forecasting techniques
Predictive analytics for demand planning
Time-series modeling and evaluation
Data-driven supply chain strategy
Course Outline
Unit 1: Introduction to ML in Forecasting
Traditional vs. machine learning forecasting methods
Benefits and challenges of ML in demand planning
Key ML algorithms for forecasting
Industry case studies
Unit 2: Data Preparation and Feature Engineering
Collecting and cleaning demand data
Handling missing values and outliers
Feature engineering for better predictions
Practical dataset preparation exercise
Unit 3: Time-Series and Predictive Modeling
Time-series analysis and ARIMA models
Regression and neural network approaches
Hybrid models for complex forecasting
Building predictive models in practice
Unit 4: Model Evaluation and Validation
Metrics for forecasting accuracy
Cross-validation and testing approaches
Avoiding overfitting and underfitting
Real-world model evaluation case study
Unit 5: Business Integration and Future of ML Forecasting
Embedding ML forecasts into supply chain planning
Using forecasts for sales and inventory optimization
Ethical and governance considerations in AI forecasting
Future trends in demand forecasting technologies
Ready to improve forecasting with machine learning?
Join the Machine Learning for Demand Forecasting Training Course with EuroQuest International Training and transform the accuracy of your business planning.
The Machine Learning for Demand Forecasting Training Courses in Geneva provide professionals with the analytical methods and practical tools needed to accurately predict future demand patterns and optimize supply chain and business planning processes. These programs are designed for data analysts, supply chain managers, financial planners, operations leaders, and strategic decision-makers who aim to enhance forecasting accuracy and improve resource allocation using data-driven techniques.
Participants explore the core principles of machine learning–based forecasting, including time series analysis, regression modeling, pattern recognition, and anomaly detection. The courses demonstrate how machine learning models can analyze historical data, identify demand drivers, detect seasonal trends, and anticipate future fluctuations with greater precision than traditional forecasting methods. Through hands-on exercises and real-world case studies, attendees learn to prepare datasets, select appropriate forecasting algorithms, evaluate model performance, and translate predictive outputs into actionable plans.
These demand forecasting training programs in Geneva emphasize both operational and strategic application. The curriculum covers inventory optimization, capacity planning, procurement scheduling, and financial forecasting workflows supported by machine learning insights. Participants also gain experience integrating forecasting models within broader business intelligence systems, enabling real-time updates and adaptive planning processes.
Interactive workshops allow participants to work directly with forecasting tools and simulation environments, testing multiple scenarios and evaluating forecast reliability under different business conditions. Ethical considerations and responsible model deployment practices are incorporated to ensure transparency and informed decision-making.
Attending these training courses in Geneva provides the advantage of engaging in a globally connected environment known for innovation, international collaboration, and strategic planning expertise. Upon completion, participants will be prepared to lead forecasting initiatives, improve operational resilience, reduce planning uncertainty, and support data-driven growth strategies—enhancing organizational efficiency and responsiveness in dynamic market environments.