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
  • 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: 2009-08-27
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2009
  • Entry requirements:

    120 credits including Inference Theory, 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 familiar with stationary time series and the autocorrelation of a time series, and know how to estimate autocorrelation based on an observed time series;

  • know methods for estimation of trend and seasonal variation;

  • be familiar with some common time series models, in particular ARIMA processes;

  • be able to estimate the parameters of ARIMA processes and know how to test the validity of the adapted model;

  • be able to make predictions, in particular for ARIMA processes;

  • know the foundations of spectral theory and how to estimate spectral density;

  • possess a basic knowledge of multivariate models, Kalman filters and non-linear models such as ARCH and GARCH models;

  • be able to use software for analysis and model adaptation 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, Kalman filter. ARCH and GARCH models. Software for analysis of time series.

    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

    Reading list

    Reading list

    Applies from: Autumn 2009

    Some titles may be available electronically through the University library.

    • Brockwell, Peter J.; Davis, Richard A. Introduction to time series and forecasting

      2. ed.: New York: Springer, 2002

      Find in the library

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