Time Series and Forecasting**
A.Y. 2023/2024
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 trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
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:
Brooks, Introductory Econometrics for Finance, 4th Edition.
Available online via: https://minerva.unimi.it/permalink/39UMI_INST/i9q3jt/alma991017361569006031
Brooks, Introductory Econometrics for Finance, 4th Edition.
Available online via: https://minerva.unimi.it/permalink/39UMI_INST/i9q3jt/alma991017361569006031
Teaching methods
Lectures and tutorials using MATLAB.
Teaching Resources
Textbook: 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.
Only for the first session, students have the opportunity:
(1) to handle a project paper.
(2) complete a set of assignments.
-> These activities contribute to the final grade and entitle you to answer fewer questions during the exam.
Grading will be as follows: exam (70%), group project (20%), assignments (10%).
The focus of the paper needs to be an application of one of the approaches discussed during the lectures.
-> After the first session, the chance to handle a paper and complete assignments will no longer be available, and the student needs to take a written test.
Assignments will be delivered via the ARIEL page of the course.
Only for the first session, students have the opportunity:
(1) to handle a project paper.
(2) complete a set of assignments.
-> These activities contribute to the final grade and entitle you to answer fewer questions during the exam.
Grading will be as follows: exam (70%), group project (20%), assignments (10%).
The focus of the paper needs to be an application of one of the approaches discussed during the lectures.
-> After the first session, the chance to handle a paper and complete assignments will no longer be available, and the student needs to take a written test.
Assignments will be delivered via the ARIEL page of the course.
Educational website(s)
Professor(s)