Course detail
Stochastic Processes
FSI-SSP Acad. year: 2021/2022 Summer semester
The course provides an introduction to the theory of stochastic processes. The following topics are dealt with: types and basic characteristics, stationarity, autocovariance function, spectral density, examples of typical processes, parametric and nonparametric methods of decomposition of stochastic processes, identification of periodic components, ARMA processes. Students will learn the applicability of the methods for the description and prediction of the stochastic processes using suitable software on PC.
Language of instruction
Czech
Number of ECTS credits
4
Supervisor
Department
Learning outcomes of the course unit
The course provides students with basic knowledge of modeling stochastic processes (decomposition, ARMA) and ways to estimate their assorted characteristics in order to describe the mechanism of the process behavior 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.
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 written or oral exam, and outcome of an individual data analysis project.
Aims
The course objective is to make students familiar with the principles of the theory of stochastic processes and models used for the analysis of time series as well as with estimation algorithms of their parameters. At seminars, students apply theoretical procedures on simulated or real data using suitable software. The semester is concluded with a project of analysis and prediction of a real stochastic process.
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.
The study programmes with the given course
Programme N-MAI-P: Mathematical Engineering, Master's, compulsory
Programme IT-MGR-2: Information Technology, Master's
branch MBI: Bioinformatics and Biocomputing, elective
Programme IT-MGR-2: Information Technology, Master's
branch MBS: Information Technology Security, elective
Programme IT-MGR-2: Information Technology, Master's
branch MMM: Mathematical Methods in Information Technology, compulsory-optional
Programme IT-MGR-2: Information Technology, Master's
branch MPV: Computer and Embedded Systems, elective
Programme IT-MGR-2: Information Technology, Master's
branch MSK: Computer Networks and Communication, elective
Type of course unit
Lecture
26 hours, optionally
Teacher / Lecturer
Syllabus
Stochastic process, types.
Strict and weak stationarity.
Autocorrelation function. Sample autocorrelation function.
Decomposition model (additive, multiplicative), variance stabilization, trend estimation in model without seasonality: (polynomial regression, linear filters)
Trend estimation in model with seasonality. Randomness tests.
Linear processes.
ARMA(1,1) processes. Asymptotic properties of the sample mean and autocorrelation function.
Best linear prediction in ARMA(1,1), Durbin-Levinson, and Innovations algorithm.
ARMA(p,q) processes, causality, invertibility, partial autocorrelation function.
Spectral density function (properties).
Identification of periodic components: periodogram, periodicity tests.
Best linear prediction, Yule-Walker system of equations, prediction error.
ARIMA processes and nonstationary stochastic processes.
Computer-assisted exercise
13 hours, compulsory
Teacher / Lecturer
Syllabus
Input, storage, and visualization of data, simulation of stochastic processes.
Moment characteristics of a stochastic process.
Detecting heteroscedasticity. Transformations stabilizing variance (power and Box-Cox transform).
Use of linear regression model on time series decomposition.
Estimation of polynomial degree for trend and separation of periodic components.
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.
Filtering by means of stepwise polynomial regression, exponential smoothing.
Randomness tests.
Simulation, identification, parameters estimate, and verification for ARMA model.
Prediction of process.
Testing significance of (partial) correlations.
Identification of periodic components, periodogram, and testing.
Tutorials on student projects.