| Dozent/in |
Dr. rer. pol. Rolf Scheufele |
| Veranstaltungsart |
Vorlesung |
| Code |
HS261065 |
| Semester |
Herbstsemester 2026 |
| Durchführender Fachbereich |
Wirtschaftswissenschaften |
| Studienstufe |
Master |
| Termin/e |
Mi, 16.09.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 23.09.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 30.09.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 07.10.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 14.10.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 21.10.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 28.10.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 04.11.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 11.11.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 18.11.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 25.11.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 02.12.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 09.12.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 16.12.2026, 16:15 - 18:00 Uhr, 2.A45
Mi, 06.01.2027, 16:15 - 17:45 Uhr, 3.B58 (Prüfung) |
| Umfang |
2 Semesterwochenstunden |
| Turnus |
weekly |
| Inhalt |
This course introduces modern methods for analyzing and forecasting economic and financial time series. It combines classical econometric approaches with contemporary machine learning and AI-based forecasting methods, including foundation models for time series forecasting.
Students learn how to explore, visualize, model, and evaluate time series data using modern statistical software (primarily R and Python). Topics include univariate and multivariate time series models, probabilistic forecasting, forecast evaluation, machine learning methods, and transformer-based forecasting models.
The course emphasizes practical applications and reproducible forecasting workflows using real-world datasets from economics and finance. Students conduct empirical forecasting projects covering data preparation, model development, validation, and communication of results. |
| Lernziele |
After completing the course, students will be able to:
1. Understand Core Time Series Concepts:
Develop a solid understanding of key concepts in time series analysis and forecasting, including stationarity, autocorrelation, seasonality, nonstationarity, and structural change.
2. Prepare, Visualize, and Analyze Time Series Data:
Learn to organize, transform, visualize, model, and interpret economic and financial time series data using modern statistical software, primarily R and Python, and reproducible empirical workflows.
3. Specify and Estimate Time Series Models:
Develop the ability to specify, estimate, and interpret univariate and multivariate time series models, including ARIMA, ARDL, VAR, and factor models.
4. Apply Machine Learning and AI Methods:
Understand and apply modern machine learning and AI-based forecasting methods, including regularization techniques, tree-based models, and transformer-based foundation models for time series forecasting.
5. Generate and Critically Evaluate Forecasts:
Learn to produce and evaluate point, interval, and probabilistic forecasts, and critically compare classical econometric and modern machine learning approaches with respect to predictive performance, interpretability, robustness, and practical applicability.
6. Conduct Empirical Forecasting Projects:
Apply theoretical and computational methods to real-world forecasting problems by conducting complete empirical forecasting projects, including data preparation, model development, validation, and communication of results. |
| Voraussetzungen |
Students are expected to have intermediate knowledge of statistics, econometrics, or data science. Basic programming skills in R or Python are recommended. |
| Sprache |
Englisch |
| Anmeldung |
Binding registration takes place via the UniPortal; see notes below in the «Proof of Performance» field.
For course information and materials, registration on the OLAT e-learning platform is required from 31 August – 25 September 2026. The students themselves are responsible for verifying the course’s creditability towards their degree program.
Direct link to the OLAT course: https://lms.uzh.ch/url/RepositoryEntry/17903616186 |
| Leistungsnachweis |
***IMPORTANT*** To receive course credit and earn academic credits, registration via the Uni Portal within the regular exam registration period (29 October – 12 November 2026) is MANDATORY.
Late registrations and withdrawals will not be accepted. Once the registration period has ended, participation in the course is MANDATORY. If the course is not completed without a valid reason and without proper withdrawal (including supporting documentation; see the Exam Guidelines), the course will be considered failed (grade 1). |
| Abschlussform / Credits |
Written exam / individual or group presentation / 3 Credits
|
| Hörer-/innen |
Nach Vereinbarung |
| Kontakt |
rolf.scheufele@snb.ch |
| Literatur |
- Diebold, F. X. (2024). Forecasting in Economics, Business, Finance and Beyond. https://www.sas.upenn.edu/~fdiebold/Teaching221/Forecasting.pdf
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: with Applications in R (2nd ed.). Springer. |