PřF:M9121 Time Series I - Course Information
M9121 Time Series I
Faculty of ScienceAutumn 2001
- Extent and Intensity
- 2/0/0. 2 credit(s). Type of Completion: z (credit).
- Teacher(s)
- doc. RNDr. Vítězslav Veselý, CSc. (lecturer)
- Guaranteed by
- prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: doc. RNDr. Vítězslav Veselý, CSc. - Prerequisites (in Czech)
- NOW( MA122 Time Series II ) && ( M7110 Statistics I || M5300 Statistics || M5301 Statistics I ) && ((( M5170 Mathematical Programming || M6170 Complex Analysis ) && M6150 Linear Functional Analysis I ) || M4140 Selected Topic in Math.Anal. )
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematics - Economics (programme PřF, M-AM)
- Mathematics (programme PřF, M-MA, specialization Applied Mathematics)
- Mathematics (programme PřF, N-MA, specialization Applied Mathematics)
- Course objectives
- Random process:
definition, examples of typical processes, consistent system of
distribution functions, Kolmogorov theorem, gaussian process,
moment characteristics (mean, autocovariance and autocorrelation
function), strict and weak stationarity, white noise,
ergodicity, special cases of stationary random processes,
properties of the autocovariance and autocorrelation function,
estimated autocovariance and autocorrelation function,
the algebraic and statistical interpretation of this estimate.
Decomposition model in time series analysis: choice of the model and its identification, the Box-Cox transformation, common methods for estimation of the deterministic components comprising both parametric methods (polynomial regression, growth curves etc.) and nonparametric methods (digital filtration, exponential weighting, spline and kernel smoothing, wavelet shrinkage etc.), randomness tests.
Identification of the periodic components: the small trend method, moving average method, the simultaneous estimate of the trend and seasonal component using linear regression, discrete Fourier transform, periodogram, periodicity tests.
Note: Computer-aided exercises are supported by the system MATLAB.
See http://www.math.muni.cz/~vesely/educ/cr1sylle.ps for more details. - Language of instruction
- Czech
- Further comments (probably available only in Czech)
- The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
General note: Současně s tímto předmětem je potřeba zapsat si v podzimnim semestru 2001 také předmět Časové řady II (MA122), který byl přesunut z jarního semestru. - Listed among pre-requisites of other courses
- Teacher's information
- http://www.math.muni.cz/~vesely/educ_cz.html#cas_rady
- Enrolment Statistics (Autumn 2001, recent)
- Permalink: https://is.muni.cz/course/sci/autumn2001/M9121