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96799 - Forecasting and Predictive Analytics

Academic Year 2026/2027

                
                        Docente:
                        Luca Trapin
                    
                        Credits:
                        10
                    
                        SSD:
                        STAT-02/A
                    
                        Language:
                        English
                    
                        Teaching Mode:
                        In-person learning (entirely or partially)
                        
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Statistics, Economics and Business (cod. 6811)

                                    Course Timetable
                                
from Sep 14, 2026 to Dec 15, 2026

Learning outcomes

At the end of the course the student has a wide knowledge of the most important statistical techniques employed for forecasting and prediction purposes in modern business activities. In particular the student is able to: - Select the most appropriate predictive model to solve the business problem at hand; - Analyze the data and perform predictions using the statistical software R; - Report the results in a proper format for the business management.

Course contents

R for statistical analysis and reproducible forecasting workflows
Foundations of statistical forecasting and time series analysis
Time series visualization, decomposition, and stationarity
Forecast diagnostics, uncertainty, and model evaluation
Regression-based forecasting for trends, seasonality, and structural breaks
Exponential smoothing methods
ARIMA and seasonal ARIMA models
Conditional variance and volatility forecasting

Readings/Bibliography

Suggested textbooks:

Diebold, F. X. (2017). Forecasting in Economics, Business, Finance and Beyond. Open textbook.
Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. Open textbook.

Teaching methods

Presentation of the theoretical and technical foundations of the forecasting methods covered in the course.
Analysis and discussion of business case studies to illustrate the application of forecasting methods to real-world problems.
Weekly laboratory sessions devoted to the implementation of forecasting techniques in R, including the use of relevant packages, guided coding activities, and discussion of results.

Assessment methods

Students are required to develop a forecasting project, submit a final report including the code and datasets, and discuss the project in an individual oral examination.

The project will be assessed based on the clarity of the empirical problem, the technical complexity of the forecasting task, and the quality of the data analysis.

The final grade will reflect both the quality of the project and the oral discussion. During the oral examination, students will also be evaluated on their technical knowledge and understanding of the statistical methods used in the project.

Teaching tools

Lecture slides
Datasets
Business case studies
R scripts

Office hours

See the website of Luca Trapin