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A course is the basic teaching unit, it's design as a medium for a student to acquire comprehensive knowledge and skills indispensable in the given field. A course guarantor is responsible for the factual content of the course.
For each course, there is a department responsible for the course organisation. A person responsible for timetabling for a given department sets a time schedule of teaching and for each class, s/he assigns an instructor and/or an examiner.
Expected time consumption of the course is expressed by a course attribute extent of teaching. For example, extent = 2 +2 indicates two teaching hours of lectures and two teaching hours of seminar (lab) per week.
At the end of each semester, the course instructor has to evaluate the extent to which a student has acquired the expected knowledge and skills. The type of this evaluation is indicated by the attribute completion. So, a course can be completed by just an assessment ('pouze zápočet'), by a graded assessment ('klasifikovaný zápočet'), or by just an examination ('pouze zkouška') or by an assessment and examination ('zápočet a zkouška') .
The difficulty of a given course is evaluated by the amount of ECTS credits.
The course is in session (cf. teaching is going on) during a semester. Each course is offered either in the winter ('zimní') or summer ('letní') semester of an academic year. Exceptionally, a course might be offered in both semesters.
The subject matter of a course is described in various texts.

MI-SCR Statistical Analysis of Time Series Extent of teaching: 2P+1C
Instructor: Completion: Z,ZK
Department: 18105 Credits: 4 Semester: Z

Annotation:
The course deals with the practical use of the basic time series modelling theory in engineering tasks, ranging from economics (stock exchange prices, employment) and industrial problems (modelling of signals and processes) to computer networks (network components load, attacks detection). The students learn to select a convenient process model, estimate its parameters, analyze its properties and use it for forecasting of future or intermediate values. The stress is put on understanding and adoption of the main principles based on practical real-world examples. Both the lab classes and the lectures exploit freely available software packages in order to provide easy and straightforward transfer of students' knowledge from the academic to the real world.

Lecture syllabus:
1. Introduction to time series, Markov processes, examples.
2. Principles of the frequentist and Bayesian probability and statistics - review.
3. Regression and autoregression models, (auto)correlation, (P)ACF, MA modely, estimation.
4. AR models from the Bayesian and frequentist viewpoints.
5. Mixed models ARMA, examples, estimation.
6. ARIMA models, special cases, examples, estimation.
7. ARIMA from the Bayesian viewpoint - structured Bayesian models.
8. Applications and analyses of AR-based models.
9. Discrete linear state-space models, Kalman filter.
10. Discrete nonlinear state-space models, extended Kalman filter, unscented filter.
11. Discrete nonlinear state-space models: sequential importance sampling, resampling, bootstrap particle filter.
12. Discrete nonlinear state-space models: particle filter extensions.
13. Exponential smoothing.

Seminar syllabus:
1. Introduction, models, forecasting, estimation, Markov process.
2. Regression and AR model, examples, various estimation methods.
3. ARMA and ARIMA models, examples.
4. Time series from the Bayesian viewpoint, examples.
5. Filtration of linear and nonlinear state-space models with Kalman filter.
6. Filtration of nonlinear models with particle filter.

Literature:
1. David Barber et al., Bayesian Time Series Models, Cambridge University Press (2011).
2. David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press (2012), ISBN 978-0-521-51814-7.
3. R. McCleary at al., Design and Analysis of Time Series Experiments, Oxford Univ. Press (2017).

Requirements:
Basic knowledge of linear algebra and mathematical analysis.

Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/MI-SCR/

The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester
MI-ZI.2016 Knowledge Engineering V Není
MI-ZI.2018 Knowledge Engineering V Není
MI-SP-TI.2016 System Programming V Není
MI-SP-SP.2016 System Programming V Není
MI-SPOL.2016 Unspecified Branch/Specialisation of Study V Není
MI-WSI-WI.2016 Web and Software Engineering V Není
MI-WSI-SI.2016 Web and Software Engineering V Není
MI-WSI-ISM.2016 Web and Software Engineering V Není
MI-NPVS.2016 Design and Programming of Embedded Systems V Není
MI-PSS.2016 Computer Systems and Networks V Není
MI-PB.2016 Computer Security V Není


Page updated 28. 3. 2024, semester: Z/2023-4, L/2019-20, L/2022-3, Z/2019-20, Z/2022-3, L/2020-1, L/2023-4, Z/2020-1, Z,L/2021-2, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška