Course overview
Traditional forecasting extrapolates from history, which works until the market changes. AI can do better, capturing seasonality, promotions, sentiment, and competitive shifts, and updating as conditions move. But it depends entirely on the quality of the data beneath it and on honest evaluation. This course covers AI-driven forecasting and market analysis end to end.
Participants examine why traditional forecasting falls short and what AI adds, the data foundations forecasting requires, and machine-learning models for demand. The course then covers AI in market trend analysis and sentiment, real-time analytics and scenario planning, and integrating AI forecasts into business planning.
Why this matters
Forecast error is expensive at both ends, excess stock ties up cash while shortages lose sales, and market shifts missed early become strategic problems. AI-driven forecasting narrows both gaps. Professionals who can build and interrogate these forecasts improve planning across the business, work that extends the methods in the Machine Learning for Demand Forecasting course.
What you will be able to do afterwards
By the end of the course, participants will be able to:
- Explain where AI improves on traditional forecasting.
- Build the data foundations and governance forecasting requires.
- Apply machine-learning models to demand forecasting.
- Use AI and sentiment analysis to detect market trends.
- Integrate AI forecasts into planning and adjust them in real time.
Course outline
Unit 1: Introduction to AI in forecasting and market analysis
The unit sets out what AI adds.
- Why traditional forecasting falls short.
- An overview of AI, machine learning, and predictive analytics.
- The business value of AI-driven forecasting.
- Case studies of AI in demand and market insight.
Unit 2: Data foundations for AI forecasting
Participants examine the data forecasts rest on.
- Identifying key data sources.
- Data quality, integrity, and governance.
- Structuring data for AI applications.
- Overcoming challenges in data collection.
Unit 3: Machine learning for demand forecasting
The unit covers the models.
- Time-series forecasting with AI models.
- Regression, neural networks, and deep learning.
- Handling seasonality and market volatility.
- A worked example of building a forecasting model.
Unit 4: AI in market trend analysis
Participants study reading the market with AI.
- Using AI to detect consumer behavior shifts.
- Sentiment analysis and social listening.
- Predictive models for market opportunities.
- A case study of AI in competitive intelligence.
Unit 5: Real-time analytics and scenario planning
The unit covers forecasting that adapts.
- The role of real-time data in forecast accuracy.
- AI-enabled scenario analysis.
- Adjusting forecasts dynamically.
- Tools for real-time visualization and dashboards.
Unit 6: Integrating AI insights into business planning
Participants connect forecasts to decisions.
- Embedding AI forecasts into supply chain planning.
- Aligning forecasts with sales and inventory decisions.
- Cross-functional use of forecast insight.
- Measuring forecast accuracy and business impact.
Unit 7: Governance and the future of AI forecasting
The closing unit looks ahead responsibly.
- Governance and ethics in AI forecasting.
- Avoiding over-reliance on models.
- Emerging technologies in forecasting.
- A roadmap for AI-driven planning.
How the course is delivered
The course combines structured teaching with documented cases, worked examples, and guided analysis of forecasting models and market data. Participants reason through building and questioning forecasts, so the methods transfer to their own planning. Deep programming skill is not required.
Who should attend
The course suits demand planners, supply chain and market analysts, marketing and commercial staff, and managers who rely on forecasts. A basic comfort with data is helpful.
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
How does this differ from the machine learning demand forecasting course?
This course widens the scope to market analysis, sentiment, and real-time scenario planning alongside demand forecasting, while the other focuses tightly on the machine-learning modeling itself. They complement each other.
Does it cover market sentiment and social listening?
Yes. Detecting consumer behavior shifts through sentiment analysis and social listening is a full topic, since these signals often move before sales data does.
Do I need to code?
No. The course focuses on the methods, the data, and interpreting forecasts, so planners and analysts can follow it without programming.
Related courses
- Predictive Analytics for Market Trends
- Demand Planning & Forecasting in Supply Chain Management
- AI-Driven Decision Making in Operations
- Augmented Analytics and AI-Driven Insights
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.
All Course Dates & Locations
27 dates · 13 cities · Oct 2026 – Jun 2027