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Lecturer(s)
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Jašková Paulína, Mgr.
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Fišerová Eva, doc. RNDr. Ph.D.
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Vencálek Ondřej, doc. Mgr. Ph.D.
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Course content
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1. Time series and random process, classification, approaches to model building. 2. Possibilities of General Linear Model in describing a trend. 3. Moving average of a general order. 4. Double exponential smoothing, comparison with simple smoothing. 5. Seasonal models, elaboration. 6. Derivation of the Model of Hidden Periodicities; periodogram and Fisher's test. 7. Box&Jenkins' approach, basic notions. 8. Moving Average process. 9. Autoregressive process. 10. General ARMA process. 11. Identification and verification.
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Learning activities and teaching methods
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Lecture, Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming)
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Learning outcomes
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Applying various methods of time-series analysis, development of statistical models.
Application Applying various methods of time-series analysis, development of statistical models.
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Prerequisites
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Basic knowledge of probability theory and mathematical statistics.
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Assessment methods and criteria
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Oral exam, Written exam
Credit: active participation in seminars, the student has to turn in individual homework Exam: the student has to present knowledge and understanding of the theory and methods
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Recommended literature
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D. Gardner a P. E. Tetlock. (2016). Superprognózy: Umění a věda předpovídání budoucnosti. Jan Melvil.
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H. Kantz, T. Schreiber. (2004). Nonlinear time series analysis. Cambridge University Press.
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J. Arlt, M. Arltová. (2009). Ekonomické časové řady. Professional Publishing.
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S. de Kok. (2016). The Future is Uncertain. [online].
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T. Cipra. (1986). Analýza časových řad s aplikacemi v ekonomii. SNTL, Praha.
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