M0130 Seminar of Random Processes

Faculty of Science
Spring 2015
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)
RNDr. Marie Forbelská, Ph.D. (lecturer)
RNDr. Radim Navrátil, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
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 8:00–10:50 MP1,01014
Prerequisites
NOW( M0122 Random Processes II )
Basics of the theory of probability, mathematical statistics, theory of estimation and the testing of hypothesis, elements of regression and correlation analysis. Computer skill: working knowledge of the numerical computing environment R.
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
Course objectives
After passing the course, the student will be able:
to apply the theory of random process to study their own problem formulation;
to use effective techniques utilized in the theory of linear models;
For statistical calculations, students learn during the seminars to use the programming environment R in detail which then, they will be able to use in practice. Seminar is located in a computer lab and use R computing environment allowing the students to get the basic practical skill. They can run demo scripts related to individual topics of the lectures as well as fit models to simulated and real data using a variety of universal procedures. The implemented algorithms are fully transparent to the students and yield unlimited opportunity for their creativity.
Syllabus
  • Regression techniques for time series data. Box-Cox transformation. Moving average and exponential smoothing. Classical decomposition methods using additive and multiplicative models. Description and summary of serial independence using autocorrelation functions. Simulation of properties of MA(q), AR(p), ARIMA(p,d,q) processes.
Literature
  • BROCKWELL, Peter J. and Richard A. DAVIS. Introduction to time series and forecasting. 2nd ed. New York: Springer, 2002, xiv, 434. ISBN 0387953515. info
  • HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
  • CIPRA, Tomáš. Analýza časových řad s aplikacemi v ekonomii. 1. vyd. Praha: Alfa, Státní nakladatelství technické literatury, 1986, 246 s., ob. info
  • ANDĚL, Jiří. Statistická analýza časových řad. Praha: SNTL, 1976. info
Teaching methods
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Participation in seminars (20%), individual final project (80%).
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
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 2016.
  • Enrolment Statistics (Spring 2015, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2015/M0130