PřF:M0122 Random Processes II - Course Information
M0122 Random Processes II
Faculty of ScienceSpring 2013
- 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)
- RNDr. Marie Forbelská, 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
- Thu 12:00–13:50 M3,01023
- Prerequisites
- M9121 Random Processes I
Basics of the theory of probability, mathematical statistics, theory of estimation and the testing of hypothesis, elements of regression and correlation analysis. - 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
- Basic concepts of linear processes are presented, including stationarity, causality, invertibility, autoregressive moving average models, and forecasting. Methods for building AR, MA and ARMA models are discussed. The course also introduces ARIMA and SARIMA models, and briefly touches on state space models and the Kalman filter. As a result of successfully completing this course, students should be able to identify Box-Jenkins models, estimate the parameters of a model, judge the adequacy of a model.
- Syllabus
- White noise, linear process, linear filter, Box-Jenkins methodology, AR, MA, ARMA procesess, causality, invertibility, the best linear prediction, modelling of the trend and seasonality by a SARIMA model, state space models, Kalman filter.
- 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
- 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
- HAMILTON, James Douglas. Time series analysis. Princeton, N.J.: Princeton University Press, 1994, xiv, 799 s. ISBN 0-691-04289-6. info
- Teaching methods
- Lectures: theoretical explanation with practical examples
- Assessment methods
- Lecture. Oral examination.
- Language of instruction
- Czech
- Further Comments
- The course is taught annually.
- Enrolment Statistics (Spring 2013, recent)
- Permalink: https://is.muni.cz/course/sci/spring2013/M0122