Lecturer(s)


Vencálek Ondřej, Mgr. Ph.D.

Müller Ivo, RNDr. PhDr. Ph.D.

Course content

1 Definition, types of time series, standard characteristics. 2 Approaches to modelling, additive and multiplicative decomposition. 3 Trend in time series, constant trend, linear trend. 4 Quadratic trend, exponential trend. 5 Exponential trend and shifted exponential trend, method of chosen points. 6 Logistic trend, Gompertz curve, measures of fit. 7 Noncentered moving averages, centered moving averages. 8 Opening and ending moving averages, prediction moving averages. 9 Analysis of periodic component, model of hidden periods, periodogram, Fisher's test. 10 Description of a seasonal component, models of constant seasons. 11 Models of proportional seasons, estimating of seasonal factors. 12 Exponential smoothing. 13 Analysis of the random component, tests of randomness.

Learning activities and teaching methods

Lecture
 Attendace
 39 hours per semester
 Preparation for the Course Credit
 20 hours per semester
 Preparation for the Exam
 50 hours per semester
 Homework for Teaching
 10 hours per semester

Learning outcomes

Applying various methods of timeseries analysis, development of statistical models.
Application Applying various methods of timeseries analysis, development of statistical models.

Prerequisites

Motivation to learn

Assessment methods and criteria

Oral exam, Written exam
Credit: active participation in seminars, written test. Exam: oral.

Recommended literature


J. Seger, R. Hindls. (1995). Statistické metody v tržním hospodářství. Victoria Publishing, Praha.
