Syllabus for Analysis of Time Series


A revised version of the syllabus is available.


  • 10 credits
  • Course code: 1MS014
  • Education cycle: Second cycle
  • 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:
  • Revised: 2018-12-18
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Spring 2019
  • Entry requirements:

    120 credits in science/engineering including Inference Theory I or Probability and Statistics. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Mathematics

Learning outcomes

On completion of the course, 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.


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.


Lectures, problem solving sessions and computer-assisted laboratory work.


Written examination (8 credit points) at the end of the course. Assignments and laboratory work (2 credit points) during the course.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

Reading list

Applies from: Spring 2022

Some titles may be available electronically through the University library.

  • Shumway, Robert H.; Stoffer, David S. Time series analysis and its applications : with R examples

    Fourth edition: [Cham]: Springer, [2017]

    Find in the library


Reading list revisions