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Course info
KIP / 9FMCR
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Course description
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Department/Unit / Abbreviation
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KIP
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9FMCR
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Fuzzy Modeling Meth. in Time Ser. Proc.
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Form of course completion
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Exam
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Form of course completion
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Exam
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Long Title
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Fuzzy Modeling Methods in Time Series Processing
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Accredited / Credits
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Yes,
15
Cred.
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Type of completion
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Oral
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Type of completion
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Oral
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Time requirements
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Lecture
26
[Hours/Semester]
Tutorial
26
[Hours/Semester]
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Course credit prior to examination
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No
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Course credit prior to examination
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No
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Automatic acceptance of credit before examination
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No
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Included in study average
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NO
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Language of instruction
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-
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / 0
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Included in study average
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NO
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Winter semester
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0 / -
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0 / -
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0 / 0
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter + Summer
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Semester taught
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Winter + Summer
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Minimum (B + C) students
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not determined
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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-
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Internship duration
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0
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| No. of hours of on-premise lessons |
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Evaluation scale |
S|N |
| Periodicity |
every year
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| Specification periodicity |
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Fundamental theoretical course |
No
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| Fundamental course |
No
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| Fundamental theoretical course |
No
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| Evaluation scale |
S|N |
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Substituted course
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KIP/8FMCR
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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The goal is to learn the methods of fuzzy modelling for analysis and forecasting of time series.
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Requirements on student
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Self-study, consultations. The course is completed with an oral exam.
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Content
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Definition of the concept of time series, examples, basic characteristics, decomposition.
Basic principles of fuzzy transform.
Basic notions of the theory of fuzzy natural logic.
Analysis of time series using fuzzy transform
Forecasting of trend and trend-cycle of time series.
Forecastint of seasonal component.
Foundations of mining information from time series.
Reduction of the dimension of time series.
Identification of periods of monotonous behaviour of time series and its evaluation.
Identification of structural breaks of time series.
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Basic:
V. Novak. Fuzzy logic with countable evaluated syntax revisited. Fuzzy Sets and Systems. 2007. ISBN 0165-0114.
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Basic:
V. Novak. Fuzzy Logic with Evaluated Syntax. In P. Cintula, C. G. Ferm uller, and C. Noguera, Handbook of Mathematical Fuzzy Logic, volume 3. College Publications, London, 2015. ISBN 1848901933.
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Basic:
Novak, V., Perfilieva, I., Dvorak, A. Insight Into Fuzzy Modeling. J. Wiley, Hoboken, USA, 2016. ISBN 9781119193180.
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Basic:
Novák, V., Perfilieva, I., Močkoř, J. Mathematical Principles of Fuzzy Logic. Fizmatlit, Moscow, 2006. ISBN 0-7923-8595-0.
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Extending:
M. Dyba and V. Novak. EQ-logics with delta connective. Iranian Journal of Fuzzy Systems, 2015. ISBN 1735-0654.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Self-tutoring
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60
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Consultation of work with the teacher/tutor (incl. electronic)
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20
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Source stuying
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60
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Preparation for an exam
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50
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Being present in classes
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52
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Semestral work
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150
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Total
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392
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Prerequisites
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Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
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| The student must know the basic principles of fuzzy modeling. |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
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| The student understands the background of the methods of fuzzy modelling for analysis and forecasting of time series. |
Skills - skills resulting from the course: |
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| The student can set parameters of the program for analysis and forecasting of time series and can interpret the results. |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
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| Oral examination |
| Analysis of mental work (correspondence assignment, presentation, instructional paper, seminary work) |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
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| G6 - Consultation with a Ph.D. student |
| Individual tutoring |
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