401-4623-00L  Time Series Analysis

SemesterAutumn Semester 2023
LecturersN. Meinshausen
Periodicitytwo-yearly recurring course
CourseDoes not take place this semester.
Language of instructionEnglish



Courses

NumberTitleHoursLecturers
401-4623-00 GTime Series Analysis
Does not take place this semester.
2 hrsN. Meinshausen

Catalogue data

AbstractThe course offers an introduction into analyzing times series, that is observations which occur in time. The material will cover Stationary Models, ARMA processes, Spectral Analysis, Forecasting, Nonstationary Models, ARIMA Models and an introduction to GARCH models.
Learning objectiveThe goal of the course is to have a a good overview of the different types of time series and the approaches used in their statistical analysis.
ContentThis course treats modeling and analysis of time series, that is random variables which change in time. As opposed to the i.i.d. framework, the main feature exibited by time series is the dependence between successive observations.

The key topics which will be covered as:

Stationarity
Autocorrelation
Trend estimation
Elimination of seasonality
Spectral analysis, spectral densities
Forecasting
ARMA, ARIMA, Introduction into GARCH models
LiteratureThe main reference for this course is the book "Introduction to Time Series and Forecasting", by P. J. Brockwell and R. A. Davis
Prerequisites / NoticeBasic knowledge in probability and statistics

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersN. Meinshausen
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationThe examination of this 2-yearly course is only offered in the two examination sessions directly following the course.
Written aidsNone
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

ProgrammeSectionType
DAS in Data ScienceStatisticsWInformation
Data Science MasterSubject-Specific ElectivesWInformation
Data Science MasterCore ElectivesWInformation
Computer Science BachelorMinor CoursesWInformation
Mathematics BachelorSelection: Probability Theory, StatisticsWInformation
Mathematics MasterSelection: Probability Theory, StatisticsWInformation
Physics BachelorElectivesWInformation
Computational Science and Engineering BachelorElectivesWInformation
Computational Science and Engineering MasterElectivesWInformation
Statistics MasterStatistical ModellingWInformation