Pre­dict­ive Ana­lyt­ics

Course content

This course provides a solid theoretical foundation for predictive modelling while placing strong emphasis on hands-on, applied analysis of economic and business data. Students work actively with real and synthetic time series in R, learning both how to produce forecasts and how to interpret, evaluate, and communicate the results in a clear and meaningful way.
 

Throughout the course, students should develop intuition for how to approach data: exploring and describing a time series, assessing data reliability and limitations, and identifying features such as trend, seasonality, outliers, and structural breaks. Understanding the data is a central step of the forecasting process, and the course integrates practical examples to highlight the types of challenges that arise in real-world settings.
 

The course covers several classes of forecasting models from different branches of statistics. In each case, the focus is on developing forecasting intuition: understanding why a model behaves as it does, the assumptions involved, when it is appropriate to apply a given method, and how to diagnose potential problems. Students learn to compare alternative models, justify their modelling choices, and critically evaluate forecasting performance using a range of diagnostic tools.

Predictive analytics plays a key role in managerial and policy decision-making.

The course therefore highlights practical applications, including forecasting sales, demand, and financial variables. Students learn how to prepare relevant data for forecasting, select well-suited models, interpret output, and communicate uncertainty to stakeholders. Tools for model selection with larger datasets are also introduced.