Course: Time Series

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Course title Time Series
Course code KMA/CASR
Organizational form of instruction Lecture + Exercise
Level of course Bachelor
Year of study not specified
Semester Winter
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory, Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Müller Ivo, RNDr. PhDr. Ph.D.
Course content
1 Definitions and types of time series, standard approaches, decomposition. 2 Linear model, trend component, polynomial trend. 3 Exponential and logistic trends, measures of model fit. 4 Moving averages, ordinary and weighted. 5 Simple and double exponential smoothing. 6 Seasonal models, constant and proportional seasonality. 7 Model of hidden periodicities, periodogram, Fisher?s test. 8 Box & Jenkins? approach, elementary notions. 9 Process of moving averages. 10 Autoregressive process. 11 Mixed process. Model development, identification and estimation of parameters. 12 Model verification. Integrated models. 13 Seasonal models. Forecasting.

Learning activities and teaching methods
Lecture
  • Attendace - 52 hours per semester
  • Preparation for the Course Credit - 20 hours per semester
  • Preparation for the Exam - 60 hours per semester
  • Homework for Teaching - 20 hours per semester
Learning outcomes
Applying various methods of time-series analysis, development of statistical models.
Application Applying various methods of time-series 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
  • T. Cipra. (1986). Analýza časových řad s aplikacemi v ekonomii. SNTL, Praha.


Study plans that include the course
Faculty Study plan (Version) Branch of study Category Recommended year of study Recommended semester