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SYLLABUS

Time Series Analysis, 6 ECTS Credits

COURSE CATEGORY   Master´s Programme in Statistics, Data Analysis and Knowledge Discovery MAIN FIELD OF STUDY   Statistik - STA SUBJECT AREA     COURSE CODE   732A34

AIM OF THE COURSE

The course provides basic skills for professional work in which time series data are explored, modified, modelled and assessed to detect trends and make forecasts. Having completed the course, the student should be able to: - use knowledge about widely used methods for the analysis of time series data, - display a good understanding of major principles for the selection, estimation and validation of time series models, - use statistical software to: (i) fit appropriate time series models to given data sets, (ii) make inference about time series components, and (iii) compute forecasts and their statistical uncertainty, - demonstrate insightful assessment of the quality of given data sets and the generalization capacity of the statistical relationships on which forecasts can be based.

CONTENTS

The course content comprises practical as well as theoretical elements, for example: - computer exercises, - time series decomposition, - autocorrelation and partial autocorrelation, - forecasting using time series regression, ARIMA models and transfer functions, - intervention analysis, - trend detection.

TEACHING

Computer exercises in which the students have access to supervision provide practical experience of data analysis. The teaching comprises lectures, seminars, and computer exercises. The lectures are devoted to presentations of theories, concepts, and methods. The seminars comprise student presentations and discussions of assignments. Language of instruction: English.

EXAMINATION

Assignments encompassing computer-based data analysis. One final written examination. Students failing an exam covering either the entire course or part of the course two times are entitled to have a new examiner appointed for the reexamination. Students who have passed an examination may not retake it in order to improve their grades.

ADMISSION REQUIREMENTS

Students entering the course should have a bachelor’s degree with a total of at least 90 ECTS credits (1.5 years of full-time studies) in mathematics, applied mathematics, statistics, and computer science. The undergraduate courses in mathematics should include both calculus and linear algebra. Basic undergraduate courses in statistics and computer science are also required. Documented knowledge of English equivalent to "Engelska B"; i.e. English as native language or an internationally recognized test, e.g. TOEFL (minimum scores: Paperbased 575 + TWE-score 4.5, internetbased 90 TWE-score 20), IELTS, academic (minimum score: Overall band 6.5 and no band under 5.5), or equivalent.

GRADING

The course is graded according to the ECTS grading scale A-F

CERTIFICATE

Course certificate is issued by the Faculty Board on request. The Department provides a special form which should be submitted to the Student Affairs Division.

COURSE LITERATURE

The course literature is decided upon by the department in question.

OTHER INFORMATION

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.

Time Series Analysis Time Series Analysis   Department responsible for the course or equivalent: MAI - Department of Mathematics             Registrar No: 1330/06-41   Course Code: 732A34           Exam codes: see Local Computer System       Subject/Subject Area : Statistik - STA                       Level   Education level     Subject Area Code   Field of Education   A1X   Advanced level     STA   SA   The syllabus was approved by the Board of Faculty of Arts and Science 2008-09-10