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
  • Applies from: Autumn 2007
  • Entry requirements:

    BSc, 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 able to

  • give an account of important concepts and definitions in the area of the course;

  • exemplify and interpret important concepts in specific cases;

  • use the theory, methods and techniques of the course to solve mathematical statistical problems;

  • express problems from relevant areas of applications in a form suitable for further mathematical statistical analysis, choose suitable models and solution techniques;

  • interpret and asses results obtained;

  • use statistical software;

  • present mathematical statistical arguments to others.

    Higher grades, 4 or 5, require a higher level of proficiency. The student should be able to treat and solve problems of greater complexity, i.e. problems requiring a combination of ideas and methods for their solution, and be able to give a more detailed account of the proofs of important theorems and by examples and counter-examples be able to motivate the scope of various results.

    Requirements concerning the student's ability to present arguments and reasoning are greater.

    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 2007

    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

    • Chatfield, Christopher The analysis of time series : an introduction

      6. ed.: Boca Raton: Chapman & Hall/CRC, cop. 2004

      Find in the library

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