Course detail

Time series analysis

FAST-DA65Acad. year: 2019/2020

Stochastic processes, mth-order probabilty distributions of stochastic processes, characteristics of stochastic process, point and interval estimate of these characteristics, stationary random processes, ergodic processes.
Decomposition of time series -moving averages, exponential smoothing, Winters seasonal smoothing.
The Box-Jenkins approach (linear process, moving average process, autoregressive process, mixed autoregression-moving average process - identification of a model, estimation of parameters, verification of a model).
Spectral density and periodogram.
The use of statistical system STATISTICA and EXCEL for time analysis.

Supervisor

RNDr. Helena Koutková, CSc.

Department

Institute of Mathematics and Descriptive Geometry (MAT)

Learning outcomes of the course unit

Not applicable.

Prerequisites

Subjects taught in the course DA03, DA62 - Probability and mathematical statistics Basics of the theory of probability, mathematical statistics and linear algebra - the normal distribution law, numeric characteristics of random variables and vectors and their point and interval estimates, principles of the testing of statistical hypotheses, solving a system of linear equations, inverse to a matrix

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

ANDĚL, J.: Statistická analýza časových řad. SNTL Praha 1976
CIPRA, T.: Analýza časových řad s aplikacemi v ekonomii. SNTL Praha 1986
KOČENDA, E., ČERNÝ, A.: Elements of time series econometrics an applied approach. Karolinum Praha 2007
PAPOULIS, A.: Random Variables and Stochastic Processes. McGraw-Hill New York 1991

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

Not applicable.

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

1. General concepts of stochastic process. Mth -order probabilty distributions of stochastic process. Characteristics of stochastic process, poin and interval estimate of these characteristics.
2. Stationary process.
3. Ergodic process.
4. Linear regression model.
5. Linear regression model.
6. Decomposition of time series. Regression approach to trend.
7. Moving average.
8. Exponential smoothing.
9. Winter´s seasonal smoothing.
10. Periodical model - spectral density and periodogram.
11. Linear process. Moving average process - MA(q).
12. Autoregressive process - AR(p).
13. Mixed autoregression - moving average process - ARMA(p,q), ARIMA(p,d,q).

Aims

After the course, the students should understand the basics of the theory of stochastic processes, know what a stochastic process is and when it is determined in terms of probability, know what numeric characteristics are of stochastic processes and they can be estimated. They should be able to decompose a time series, estimate its components and make forecats, judge the periodicity of a process.
Using statistical programs, they should be able to identify Box-Jenkins models, estimate the parameters of a model, judge the adequacy of a model and construct forecasts.

Specification of controlled education, way of implementation and compensation for absences

Extent and forms are specified by guarantor’s regulation updated for every academic year.

Classification of course in study plans

  • Programme D-P-E-SI (N) Doctoral

    branch PST , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-E-SI (N) Doctoral

    branch PST , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-C-SI (N) Doctoral

    branch PST , 2. year of study, winter semester, 10 credits, elective

  • Programme D-P-C-SI (N) Doctoral

    branch PST , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-E-SI (N) Doctoral

    branch MGS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-P-E-SI (N) Doctoral

    branch MGS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-C-SI (N) Doctoral

    branch KDS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-P-C-SI (N) Doctoral

    branch KDS , 2. year of study, winter semester, 10 credits, elective
    branch MGS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-C-SI (N) Doctoral

    branch MGS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-E-SI (N) Doctoral

    branch FMI , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-C-SI (N) Doctoral

    branch FMI , 2. year of study, winter semester, 10 credits, elective

  • Programme D-P-E-SI (N) Doctoral

    branch FMI , 2. year of study, winter semester, 10 credits, elective

  • Programme D-P-C-SI (N) Doctoral

    branch FMI , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-E-SI (N) Doctoral

    branch KDS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-P-E-SI (N) Doctoral

    branch KDS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-P-C-SI (N) Doctoral

    branch VHS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-P-E-SI (N) Doctoral

    branch VHS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-C-SI (N) Doctoral

    branch VHS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-E-SI (N) Doctoral

    branch VHS , 2. year of study, winter semester, 10 credits, elective

  • Programme D-K-C-GK Doctoral

    branch GAK , 2. year of study, winter semester, 10 credits, elective

  • Programme D-P-C-GK Doctoral

    branch GAK , 2. year of study, winter semester, 10 credits, elective

Type of course unit

 

Lecture

39 hours, optionally

Teacher / Lecturer

RNDr. Helena Koutková, CSc.

eLearning

eLearning: currently opened course