Course detail

Stochastic Processes

FSI-SSPAcad. year: 2018/2019

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

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 time series observations. 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.

Recommended optional programme components

Not applicable.

Recommended or required reading

Ljung, L. System Identification-Theory For the User. 2nd ed. PTR Prentice Hall : Upper Saddle River, 1999. (EN)
Cipra, Tomáš. Analýza časových řad s aplikacemi v ekonomii. 1. vyd. Praha : SNTL - Nakladatelství technické literatury, 1986. 246 s. (CS)
Anděl, Jiří. Statistická analýza časových řad. Praha : SNTL, 1976. (CS)
Brockwell, P.J. - Davis, R.A. Introduction to time series and forecasting. 2nd ed. New York : Springer, 2002. xiv, 434 s. ISBN 0-387-95351-5. (EN)

Planned learning activities and teaching methods

The course is taught through lectures explaining the basic principles and theory of the discipline. Exercises are focused 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 oral exam and outcome of an individual data analysis project.

Language of instruction

Czech

Work placements

Not applicable.

Aims

The course objective is to make students familiar with principles of theory 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.

Classification of course in study plans

  • Programme IT-MGR-2 Master's

    branch MBI , any year of study, summer semester, 4 credits, optional
    branch MPV , any year of study, summer semester, 4 credits, optional
    branch MSK , any year of study, summer semester, 4 credits, optional
    branch MBS , any year of study, summer semester, 4 credits, optional
    branch MMI , any year of study, summer semester, 4 credits, optional
    branch MMM , any year of study, summer semester, 4 credits, compulsory-optional

  • Programme M2A-P Master's

    branch M-MAI , 1. year of study, summer semester, 4 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

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 hours, compulsory

Teacher / Lecturer

Syllabus

1. Counting, 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.

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