M0130 Seminar of Random Processes

Faculty of Science
Spring 2016
Extent and Intensity
0/3/0. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: z (credit).
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 of Seminar Groups
M0130/01: Fri 12:00–14:50 MP1,01014, D. Kraus
Prerequisites
NOW( M0122 Random Processes II )
Basics of probability theory, mathematical statistics, theory of estimation and hypotheses testing, regression and correlation analysis, working knowledge of R software
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
This course consists of exercises and computer practicals on topics in time series analysis. With the help of theoretical problems, simulation and real data analysis, the students will deepen their understanding of time series methods and learn to recognize situations that can be addressed by a time series techniques, choose an appropriate model, implement it in R software and interpret the results.
Syllabus
  • Regression, smoothing and decomposition techniques
  • ARMA models and their extensions (ARIMA, SARIMA)
  • Spectral methods
  • 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. Introduction to time series and forecasting. 2nd ed. New York: Springer, 2002, xiv, 434. ISBN 0387953515. 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
Exercises and computer practicals
Assessment methods
Coursework, project
Language of instruction
Czech
Further Comments
The course is taught annually.
Teacher's information
https://is.muni.cz/auth/el/1431/jaro2016/M0130/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.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2016/M0130