Syllabus for Analysis of Time Series
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A revised version of the syllabus is available.
Syllabus
- 10 credits
- Course code: 1MS014
- Education cycle: Second cycle
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Main field(s) of study and in-depth level:
Mathematics A1N,
Financial Mathematics A1N
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
First cycle
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
Second cycle
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2007-03-15
- Established by: The Faculty Board of Science and Technology
- Revised: 2013-04-24
- Revised by: The Faculty Board of Science and Technology
- Applies from: Autumn 2013
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Entry requirements:
120 credits including Inference Theory I, or Probability and Statistics and Stochastic Modelling
- Responsible department: Department of Mathematics
Learning outcomes
In order to pass the course (grade 3) the student should be able to
- give an account for the concepts stationary time series and autocorrelation and know how to estimate autocorrelation based on an observed time series;
- apply methods for estimation of trend and seasonal variation in time series;
- estimate parameters of ARIMA-processes and assess the validity of the fitted models.
- make predictions, in particular for ARIMA-processes;
- explain the foundations of spectral theory and how to estimate spectral density;
- evaluate results from statistical computer software (for example R) for model fitting of time series.
Content
Stationary time series. ARIMA processes. Box–Jenkin’s method for model adaptation. Prediction. Seasonal modelling. Spectral theory, smoothing methods for spectral estimation. Software for analysis of time series. Overview of multivariate models, Kalman-filters och non-linear models such as ARCH- and GARCH-models.
Instruction
Lectures, problem solving sessions and computer-assisted laboratory work.
Assessment
Written examination (8 credit points) at the end of the course. Assignments and laboratory work (2 credit points) during the course.
Syllabus Revisions
- Latest syllabus (applies from Autumn 2022)
- Previous syllabus (applies from Spring 2019, version 2)
- Previous syllabus (applies from Spring 2019, version 1)
- Previous syllabus (applies from Autumn 2013)
- Previous syllabus (applies from Autumn 2009)
- Previous syllabus (applies from Autumn 2008, version 3)
- Previous syllabus (applies from Autumn 2008, version 2)
- Previous syllabus (applies from Autumn 2008, version 1)
- Previous syllabus (applies from Autumn 2007, version 2)
- Previous syllabus (applies from Autumn 2007, version 1)
Reading list
Reading list
Applies from: Autumn 2013
Some titles may be available electronically through the University library.
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Shumway, Robert H.;
Stoffer, David S.
Time series analysis and its applications : with R examples
Fourth edition: [Cham]: Springer, [2017]
Mandatory