General introduction to time series. Stationary and non-stationary models, e.g. ARMA- and ARIMA-models. Projections and prediction of time series. Spectral theory. Estimation of parameters and spectra. Models on state-space form and Kalman filtering.
SF2943 Time Series Analysis 7.5 credits
Information per course offering
Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.
Information for Spring 2026 Start 16 Mar 2026 programme students
- Course location
KTH Campus
- Duration
- 16 Mar 2026 - 1 Jun 2026
- Periods
- P4 (7.5 hp)
- Pace of study
50%
- Application code
60326
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
Elective for all programmes as long as it can be included in your programme.
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
Master's Programme, Applied and Computational Mathematics, åk 1, Optional
Master's Programme, Applied and Computational Mathematics, åk 1, DAVE, Conditionally Elective
Master's Programme, Applied and Computational Mathematics, åk 1, FMIA, Conditionally Elective
Master's Programme, Biostatistics and Data Science, åk 1, Conditionally Elective
Master's Programme, Embedded Systems, åk 1, INMV, Recommended
Master's Programme, Industrial Engineering and Management, åk 1, FMIB, Conditionally Elective
Master's Programme, Industrial Engineering and Management, åk 1, MAIG, Conditionally Elective
Master's Programme, Machine Learning, åk 1, Conditionally Elective
Master's Programme, Mathematics, åk 1, Optional
Master's Programme, Systems, Control and Robotics, åk 1, Recommended
Master's Programme, Systems, Control and Robotics, åk 2, Recommended
Contact
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus SF2943 (Spring 2022–)Headings with content from the Course syllabus SF2943 (Spring 2022–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
The goal of the course is to provide a basic understanding of theoretical and practical tools for analyzing time series. Computer-aided projects form an essential learning activity.
In order to pass the course, the student shall be able to:
- solve problems that require knowledge and understanding of basic concepts and models in the theory of time series, in particular models of ARMA type,
- apply theoretical and practical tools to analyze time series data, quantify uncertainty in estimated models and predict future values based on observed data,
- apply mathematical theory to analyze and explain properties of different time series models, and
- account for conclusions from analyses of time series data and critically evaluate such analyses from a scientific perspective.
Literature and preparations
Specific prerequisites
- English B / English 6
- Completed basic course in probability theory and mathematical statistics (SF1918, SF1922 or equivalent).
Recommended prerequisites
Completed courses in
- Numerical methods(SF1544, Sf1545 or similar),
- Differential equations(SF1633, SF1683 or similar),
- Probability andstatistics (SF2940 or similar) and
- Regression analysis (SF2930 or similar).
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
Grading scale
A, B, C, D, E, FX, F
Examination
- OVN1 - Assignments, 3.0 credits, grading scale: P, F
- TENA - Examination, 4.5 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
Mathematics
Education cycle
Second cycle
Supplementary information
Replaces SF2945.