Machine Learning for Demand Forecasting Training Course

Course overview

Demand forecasting has moved well beyond spreadsheets and simple moving averages. Machine learning can capture seasonality, promotions, weather, and other signals that traditional methods miss, but only when the data is prepared properly and the models are validated with discipline. This course shows how machine learning is applied to demand forecasting in practice, and where it helps and where it does not.

Participants work through the full modeling path: understanding when machine learning beats classical methods, cleaning and engineering data, building time-series and predictive models, and evaluating them honestly. The course closes on the part that decides whether a model delivers value, integrating forecasts into planning, inventory, and sales decisions, and doing so responsibly.

Why this matters for planning

Forecast error is expensive at both ends: too much stock ties up cash and space, too little means lost sales and expediting costs. Better forecasts ripple through the whole supply chain. Teams that understand both the modeling and the business context can put machine learning to work without over-trusting it, a balance that complements the process view in the Demand Planning & Forecasting in Supply Chain Management course.

What you will be able to do afterwards

By the end of the course, participants will be able to:

• Judge when machine learning is the right tool for a forecasting problem.
• Prepare demand data and engineer features that improve predictions.
• Build time-series and predictive models using common algorithms.
• Evaluate forecast accuracy and avoid overfitting.
• Integrate forecasts into planning while managing the risks of automation.

Course outline

Unit 1: Introduction to machine learning in forecasting

The unit sets out where machine learning fits against traditional methods.

• Traditional versus machine-learning forecasting approaches.
• The benefits and challenges of machine learning in demand planning.
• Key algorithms used for forecasting.
• Industry case studies.

Unit 2: Data preparation and feature engineering

Participants examine the data work that determines model quality.

• Collecting and cleaning demand data.
• Handling missing values and outliers.
• Feature engineering for better predictions.
• A worked dataset-preparation example.

Unit 3: Time-series and predictive modeling

The unit covers the core modeling techniques.

• Time-series analysis and ARIMA models.
• Regression and neural-network approaches.
• Gradient boosting and hybrid models for complex demand.
• Building predictive models in practice.

Unit 4: Model evaluation and validation

Participants learn to test models honestly before trusting them.

• Accuracy metrics such as MAPE and RMSE.
• Cross-validation and testing approaches.
• Avoiding overfitting and underfitting.
• A real-world model-evaluation case study.

Unit 5: Business integration and the future of ML forecasting

The closing unit turns models into decisions.

• Embedding machine-learning forecasts into supply-chain planning.
• Using forecasts for sales and inventory optimization.
• Ethics and governance in AI-driven forecasting.
• Future trends in demand-forecasting technology.

How the course is delivered

The course combines structured teaching with worked examples and guided analysis of real forecasting datasets and model results. Participants follow the reasoning from raw data to validated forecast, so the methods are understandable and repeatable. Concepts are explained clearly for those without a data-science background, while the modeling detail rewards those who have one.

Who should attend

The course suits demand planners, supply-chain and inventory analysts, data and business analysts moving into forecasting, and managers who commission or rely on forecasts. A basic comfort with data is helpful; deep programming experience is not required to follow the concepts.

About EuroQuest International Training

EuroQuest International Training is an international training provider founded in 2015, with a catalog of more than 1,000 courses delivered to over 15,000 participants. Headquartered in Bratislava, EuroQuest runs courses across a network of European and regional training hubs and focuses on practical, current, and professionally relevant content.

Frequently asked questions

Do I need coding skills to take this course?

No. The course explains the concepts and workflow of machine-learning forecasting so planners and analysts can follow it. Those who write code will get more from the modeling detail, but coding is not required to understand the material.

Does the course replace my existing forecasting process?

No. It shows where machine learning can improve or complement current methods, and stresses honest evaluation, so you can decide what to adopt rather than assume machine learning is always better.

Is the course tied to one software tool?

The concepts are tool-neutral and apply across common platforms. The focus is on the methods and judgment rather than the syntax of any single package.

Related courses

• Predictive Analytics & Demand Forecasting in Logistics
• Machine Learning for Business Intelligence
• Big Data Analytics and Predictive Modeling
• AI-Driven Demand Forecasting & Market Analysis

Register for this course

To reserve a place or request an in-house session for your team, contact EuroQuest International Training and our team will help you confirm dates and details.