Course detail

Stochastic Processes

FSI-SSPAcad. year: 2019/2020

The course provides the introduction to the theory of stochastic processes. The following topics are dealt with: types and basic characteristics, covariance function, spectral density, stationarity, examples of typical processes, time series and their evaluation, parametric and nonparametric methods, identification of periodic components, ARMA processes. Applications of methods for elaboration of project time series evaluation and prediction supported by the computational system MATLAB.

Language of instruction

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

doc. RNDr. Vítězslav Veselý, CSc.

Department

Institute of Mathematics (ÚM)

Learning outcomes of the course unit

The course provides students with basic knowledge of modelling of stochastic processes (decomposition, ARMA) and ways of estimate calculation of their assorted characteristics in order to describe the mechanism of the process behaviour on the basis of its sample path. Students learn basic methods used for real data evaluation.

Prerequisites

Rudiments of the differential and integral calculus, probability theory and mathematical statistics.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focussed on practical topics presented in lectures.

Assesment methods and criteria linked to learning outcomes

Graded course-unit credit requirements: active participation in seminars, demonstration of basic skills in practical data analysis on PC, evaluation is based on the written or oral exam and outcome of an individual data analysis project.

Course curriculum

Not applicable.

Work placements

Not applicable.

Aims

The course objective is to make students familiar with principles of the theory of stochastic processes and models used for analysis of time series as well as with estimation algorithms of their parameters. At seminars students practically apply theoretical procedures on simulated or real data using the software MATLAB. Result is a project of analysis and prediction of real time series.

Specification of controlled education, way of implementation and compensation for absences

Attendance at seminars is compulsory whereas the teacher decides on the compensation for absences.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Brockwell, P.J. - Davis, R.A. Introduction to time series and forecasting. 3rd ed. New York: Springer, 2016. 425 s. ISBN 978-3-319-29852-8. (EN)
Brockwell, P.J. - Davis, R.A. Time series: Theory and Methods. 2-nd edition 1991. New York: Springer. ISBN 978-1-4419-0319-8. (EN)

Recommended reading

Hamilton, J.D. Time series analysis. Princeton University Press, 1994. xiv, 799 s. ISBN 0-691-04289-6. (EN)
Ljung, L. System Identification-Theory For the User. 2nd ed. PTR Prentice Hall : Upper Saddle River, 1999. (EN)

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme M2A-P Master's

    branch M-MAI , 1 year of study, summer semester, compulsory

  • Programme IT-MSC-2 Master's

    branch MBI , 0 year of study, summer semester, elective
    branch MBS , 0 year of study, summer semester, elective
    branch MMI , 0 year of study, summer semester, elective
    branch MSK , 0 year of study, summer semester, elective
    branch MMM , 0 year of study, summer semester, compulsory-optional
    branch MPV , 0 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. RNDr. Vítězslav Veselý, CSc.

Syllabus

1. Stochastic process, types, trajectory, examples.
2. Consistent system of distribution functions, strict and weak stacionarity.
3. Moment characteristics: mean and autocorrelation function.
4. Spectral density function (properties).
5. Decomposition model (additive, multiplicative), variance stabilization.
6. Identification of periodic components: periodogram, periodicity tests.
7. Methods of periodic components separation.
8. Methods of trend estimation: polynomial regression, linear filters, splines.
9. Tests of randomness.
10.Best linear prediction, Yule-Walker system of equations, prediction error.
11.Partial autocorrelation function, Durbin-Levinson and Innovations algorithm.
12.Linear systems and convolution, causality, stability, response.
13.ARMA processes and their special cases (AR and MA process).

Computer-assisted exercise

13 hod., compulsory

Teacher / Lecturer

doc. RNDr. Vítězslav Veselý, CSc.

Syllabus

1. Input, storage and visualization of data, moment characteristics of stochastic process.
2. Simulating time series with some typical autocorrelation functions: white noise, coloured noise with correlations at lag one, exhibiting linear trend and/or periodicities.
3. Detecting heteroscedasticity. Transformations stabilizing variance (power and Box-Cox transform).
4. Identification of periodic components, periodogram, and testing.
5. Use of linear regression model on time series decomposition.
6. Estimation of polynomial degree for trend and separation of periodic components.
7. Denoising by means of linear filtration (moving average): design of optimal weights preserving polynomials up to a given degree, Spencer's 15-point moving average.
8. Filtering by means of stepwise polynomial regression.
9. Filtering by means of exponential smoothing.
10.Randomness tests.
11.Simulation, identification, parameters estimate and verification for ARMA model.
12.Testing significance of (partial) correlations.
13.Tutorials on student projects.

Elearning

eLearning: currently opened course