PřF:M0122 Time Series II - Course Information
M0122 Time Series II
Faculty of Sciencespring 2018
- Extent and Intensity
- 2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
- Teacher(s)
- doc. Mgr. David Kraus, Ph.D. (lecturer)
- Guaranteed by
- doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science - Timetable
- Tue 16:00–17:50 M3,01023
- Timetable of Seminar Groups:
- Prerequisites
- M9121 Time Series I
Calculus, linear algebra, basics of probability theory and mathematical statistics, theory of estimation and hypotheses testing, linear regression, basic methods of time series analysis, knowledge of R software - Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematics - Economics (programme PřF, M-AM)
- Mathematics (programme PřF, M-MA, specialization Applied Mathematics)
- Mathematics (programme PřF, N-MA, specialization Applied Mathematics)
- Course objectives
- The course offers a coverage of selected advanced methods and models for time series. The course covers theoretical foundations, statistical models and inference, software implementation, application and interpretation.
- Learning outcomes
- The students will gain a deeper understanding of the methods and their relations and learn to recognize situations that can be addressed by the models discussed in the course, choose an appropriate model, implement it and interpret the results.
- Syllabus
- Multivariate time series
- State-space models
- Spectral analysis of univariate and multivariate time series
- Special models
- Literature
- BROCKWELL, Peter J. and Richard A. DAVIS. Time series :theory and methods. 2nd ed. New York: Springer-Verlag, 1991, xvi, 577 s. ISBN 0-387-97429-6. info
- COWPERTWAIT, Paul S. P. and Andrew V. METCALFE. Introductory time series with R. New York, N.Y.: Springer, 2009, xv, 254. ISBN 9780387886978. info
- CRYER, Jonathan D. and Kung-Sik CHAN. Time series analysis : with applications in R. 2nd ed. [New York]: Springer, 2008, xiii, 491. ISBN 9780387759586. info
- FORBELSKÁ, Marie. Stochastické modelování jednorozměrných časových řad. 1. vyd. Brno: Masarykova univerzita, 2009, iii, 245. ISBN 9788021048126. info
- HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
- SHUMWAY, Robert H. and David S. STOFFER. Time Series Analysis and Its Applications: With R Examples. Third Edition. New York: Springer-Verlag, 2011. Available from: https://dx.doi.org/10.1007/978-1-4419-7865-3. URL info
- Teaching methods
- Lectures, exercises, practical project
- Assessment methods
- Bonus midterm written exam (score B between 0 and 100).
Final written exam (score F between 0 and 100).
Total score T is defined as 0.75*F + 0.25*max(F,B) rounded to the nearest integer.
Score-to-grade conversion: A for T in [91,100], B for T in [81,90], C for T in [71,80], D for T in [61,70], E for T in [51,60], F for T in [0,50]. - Language of instruction
- Czech
- Further Comments
- Study Materials
The course is taught annually. - Teacher's information
- https://is.muni.cz/auth/el/1431/jaro2018/M0122/index.qwarp
- Enrolment Statistics (spring 2018, recent)
- Permalink: https://is.muni.cz/course/sci/spring2018/M0122