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Course, academic year 2023/2024
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Time Series - NSTP007
Title: Časové řady
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018
Semester: summer
E-Credits: 6
Hours per week, examination: summer s.:4/0, Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: cancelled
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Guarantor: prof. RNDr. Tomáš Cipra, DrSc.
Classification: Mathematics > Probability and Statistics
Interchangeability : NMST537
Is co-requisite for: NSTP165
Is incompatible with: NSTP006
Annotation -
Last update: T_KPMS (13.05.2010)
Basic methods of time series analysis and their applications, time series decomposition and adaptive techniques, Box-Jenkins methodology including ARIMA and seasonal models, financial time series (models of volatility and nonlinear in mean), multivariate time series (vector autoregression, Kalman filter). Most of the methods are applied in a facultative seminar. Requirements: Basic knowledge of statistics.
Aim of the course -
Last update: T_KPMS (09.05.2008)

The students should master the most important methods of practical time series analysis so that they are capable to apply them in practice.

Literature - Czech
Last update: T_KPMS (13.05.2010)

Cipra, T.: Analýza časových řad s aplikacemi v ekonomii. SNTL/ALFA, Praha 1986

Cipra, T.: Finanční ekonometrie. Ekopress, Praha 2008

Teaching methods -
Last update: G_M (27.05.2008)


Syllabus -
Last update: T_KPMS (13.05.2010)

I. Classification of random processes.

II. Decomposition methods: 1. Trend. 2. Seasonality and periodicity. 3. Tests of randomness.

III. Box-Jenkins methodology 1. ARMA models ARMA 2. Identification, estimation, verification and prediction. 3. ARIMA and seasonal models.

IV. Financial time series: 1. Models of volatility (GARCH). 2. Models nonlinear in mean.

V. Multivariate time series (vector autoregression, Kalman filter).

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