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Applied Time Series
Last Updated: 2026-06-03 00:14:13
Abstract
The course starts with an introduction to time series analysis (examples, goal, mathematical notation). In the following, descriptive techniques, modeling and prediction as well as advanced topics will be covered.
Objective
Getting to know the mathematical properties of time series, as well as the requirements, descriptive techniques, models, advanced methods and software that are necessary such that the student can independently run an applied time series analysis.
Content
The course starts with an introduction to time series analysis that comprises of examples and goals. We continue with notation and descriptive analysis of time series. A major part of the course will be dedicated to modeling and forecasting of time series using the flexible class of ARMA models. More advanced topics that will be covered in the following are time series regression, time series classification and spectral analysis.
Resources
Lecture Notes
A script will be available.
General Information
- Language
- English
- Levels
- BSC , MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 120 minutes
- Aids
- written aid of max. 20 pages with arbitrary content (format A4, 10 sheets printed on front and back or 20 pages only front-printed, can be machine-written), and a pocket calculator without communication possibilities.
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Applied Time Series |
|
2 h weekly |
| exercise | Applied Time Series |
|
1 h weekly |
Offered In
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed. All courses listed as core courses (not electives) for one of the following ETH MSc programmes, MSc Statistics, MSc Physics, MSc Computer Science, MSc (Applied) Mathematics, MSc Neural Systems and Computation, MSc Robotics, Systems, and Control, MSc Data Science, MSc Electrical Engineering and Information Technology, can be taken as an elective course in the MSc CSE without prior permission.)
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Quantitative Finance Master (see Students in the Joint Degree Master's Programme "Quantitative Finance" must book UZH modules directly at the UZH. Those modules are not listed here.)
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MF (Mathematical Methods in Finance) (For possible additional course offerings see )
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