PřF:M9121 Time Series I - Course Information

M9121 Time Series I

Faculty of Science
Autumn 1999

Extent and Intensity

2/0/0. Type of Completion: -.

Teacher(s)

doc. RNDr. Vítězslav Veselý, CSc. (lecturer)

Guaranteed by

Prerequisites (in Czech)

Course Enrolment Limitations

The course is only offered to the students of the study fields the course is directly associated with.

fields of study / plans the course is directly associated with

Syllabus

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 is taught annually.
The course is taught: every week.

Listed among pre-requisites of other courses

Teacher's information

http://www.math.muni.cz/~vesely/educ_cz.html#cas_rady