Time Series and Forecasting**
A.Y. 2025/2026
Learning objectives
Forecasting time series data is of critical importance for a variety of decision-makers, and this course will focus on methodologies that can be applied to developing models for forecasting time series in a multitude of settings and applications.
Expected learning outcomes
Upon completing this module, you will have the skills to:
1. Construct and validate both univariate and multivariate time series models.
2. Leverage time series models for forecasting future values.
3. Assess and compare forecasts generated by various models.
4. Generate point and density forecasts.
1. Construct and validate both univariate and multivariate time series models.
2. Leverage time series models for forecasting future values.
3. Assess and compare forecasts generated by various models.
4. Generate point and density forecasts.
Lesson period: First four month period
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First four month period
Course syllabus
Basics of forecasting and forecast evaluation
Forecasting with deterministic variables: trends and dummy variables
Forecasting with seasonal and non-seasonal ARMA models
Forecasting trends
Forecasting with dependent variables
Nonlinear models
Multivariate forecasting methods
Forecasting with deterministic variables: trends and dummy variables
Forecasting with seasonal and non-seasonal ARMA models
Forecasting trends
Forecasting with dependent variables
Nonlinear models
Multivariate forecasting methods
Prerequisites for admission
Although formal prerequisites are not necessary for this course, a fundamental understanding of matrix algebra, statistical inference, and econometrics will significantly improve your learning experience. In case you feel the need to review these topics, I suggest consulting the following textbook:
Teaching methods
Lectures and tutorials using MATLAB.
Teaching Resources
extbook: Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. Available online at: https://otexts.com/fpp3/
Matlab: https://work.unimi.it/servizi/servizi_tec/79539.htm
Supplementary materials might be provided during the course on the ARIEL platform
Matlab: https://work.unimi.it/servizi/servizi_tec/79539.htm
Supplementary materials might be provided during the course on the ARIEL platform
Assessment methods and Criteria
Written Exam
(An exception is made for the first session.)
First Exam Session - Alternative Evaluation Path
Only for the first session, students may choose an alternative to the standard written exam. This includes:
Project Paper: developed in groups of 2,
Oral Presentation: based on the project paper,
Individual Assignments.
Grading Breakdown (First Session Only):
Project Paper: 70%
Oral Presentation: 20%
Assignments: 10%
The project paper must focus on a time series forecasting problem.
The project topic will be defined during Week 3 of the course.
The oral presentation will take place at the end of the course. Each student must present individually in class and be prepared to answer questions about the project's content and results.
Important: After the first exam session, this alternative path will no longer be available. Students will need to take a written exam for all subsequent sessions.
Assignment Submission:
Assignments will be delivered through the ARIEL page of the course.
(An exception is made for the first session.)
First Exam Session - Alternative Evaluation Path
Only for the first session, students may choose an alternative to the standard written exam. This includes:
Project Paper: developed in groups of 2,
Oral Presentation: based on the project paper,
Individual Assignments.
Grading Breakdown (First Session Only):
Project Paper: 70%
Oral Presentation: 20%
Assignments: 10%
The project paper must focus on a time series forecasting problem.
The project topic will be defined during Week 3 of the course.
The oral presentation will take place at the end of the course. Each student must present individually in class and be prepared to answer questions about the project's content and results.
Important: After the first exam session, this alternative path will no longer be available. Students will need to take a written exam for all subsequent sessions.
Assignment Submission:
Assignments will be delivered through the ARIEL page of the course.
Professor(s)