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.
NI-SCR Statistical Analysis of Time Series Extent of teaching: 2P+1C Instructor: Dedecius K. Completion: Z,ZK Department: 18105 Credits: 5 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, exponential smoothing, 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 - ETS models. Seminar syllabus:
1. Introduction, models, forecasting, estimation. 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. Barber, D. et al. : Bayesian Time Series Models. Cambridge University Press, 2011. ISBN 978-0521196765. 2. Simon, S. : Optimal State Estimation: Kalman, H-infnity and Nonlinear Approaches. Wiley, 2017. ISBN 987-0471708582. 3. McCleary, R. et al. : Design and Analysis of Time Series Experiments. Oxford University Press, 2017. ISBN 978-0190661564. Requirements:
Basic knowledge of linear algebra (BI-LIN), mathematical analysis (BI-ZMA) and probability and statistics (BI-PST).
Informace o předmětu a výukové materiály naleznete na https://courses.fit.cvut.cz/NI-SCR/ The course is also part of the following Study plans:
Study Plan Study Branch/Specialization Role Recommended semester NI-PSS.2020 Computer Systems and Networks V 3 NI-SP.2020 System Programming V 3 NI-SP.2023 System Programming V 3 NI-SPOL.2020 Unspecified Branch/Specialisation of Study VO 3 NI-TI.2023 Computer Science V 3 NI-TI.2020 Computer Science V 3 NI-PB.2020 Computer Security V 3 NI-NPVS.2020 Design and Programming of Embedded Systems V 3 NI-MI.2020 Managerial Informatics V 3 NI-SI.2020 Software Engineering (in Czech) V 3 NI-ZI.2020 Knowledge Engineering PS 3 NI-WI.2020 Web Engineering V 3 NI-SPOL.2020 Unspecified Branch/Specialisation of Study V 3 NIE-DBE.2023 Digital Business Engineering VO 3
Page updated 17. 4. 2024, semester: Z/2021-2, L/2019-20, Z/2020-1, Z/2022-3, Z/2019-20, L/2022-3, Z/2024-5, Z/2023-4, L/2021-2, L/2020-1, L/2023-4, Send comments to the content presented here to Administrator of study plans Design and implementation: J. Novák, I. Halaška