M0122 Random Processes II

Faculty of Science
Spring 2016
Extent and Intensity
2/0/0. 2 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. David Kraus, Ph.D. (lecturer)
RNDr. Marie Forbelská, Ph.D. (assistant)
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
Wed 16:00–17:50 M4,01024
Prerequisites
M9121 Random Processes I
Basics of probability theory, mathematical statistics, theory of estimation and hypotheses testing, regression and correlation analysis, basic methods of time series analysis
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course offers a comprehensive coverage of basic models for stationary time series and presents advanced methods for general time series, including theory, software implementation, application and interpretation. The students are taught 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
  • ARMA models
  • Extensions of ARMA models (ARIMA, SARIMA)
  • Models for heteroskedastic series (GARCH)
  • Methods for multivariate series (vector autoregression, cointegration)
  • State-space methods, Kálmán filter
Literature
  • 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
  • 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
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • ENDERS, Walter. Applied Econometric Time Series. 4th Edition. New York: Wiley, 2014. info
  • FORBELSKÁ, Marie. Stochastické modelování jednorozměrných časových řad. 1. vyd. Brno: Masarykova univerzita, 2009, iii, 245. ISBN 9788021048126. info
Teaching methods
Lectures
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
The course is taught annually.
Teacher's information
https://is.muni.cz/auth/el/1431/jaro2016/M0122/index.qwarp
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2016, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2016/M0122