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Lecturer(s)
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Ševčíková Paulína, Mgr.
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Vencálek Ondřej, doc. Mgr. Ph.D.
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Pavlů Ivana, Mgr. Ph.D.
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Course content
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1. How to evaluate the quality of predictions in time series - different criteria and their advantages, Brier score 2. Holt-Winters method - an adaptive approach to modeling a time series containing a seasonal component 3. Growth curves 4. Detection of a change point in a time series 5. Models of structural change, joinpoint regression 6. Volatility modeling in economic time series - ARCH and GARCH models 7. Demand modeling and warehouse management, quantile regression 8. Prediction using ensamble-methods, predictions contests 9. Kalman filter 10. Bayesian approach to time series analysis
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Learning activities and teaching methods
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unspecified
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Learning outcomes
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Applying various methods of time-series analysis, development of statistical models.
Students will have an overview of basic principles and methods of time series analysis and will be able to use these methods.
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Prerequisites
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Knowledge of the basics of probability theory and statistics.
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Assessment methods and criteria
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unspecified
Credit: active participation in exercises - presentation of analyzed data Exam: understanding of the discussed time series analysis methods including orientation in theory and calculation methods.
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Recommended literature
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A. Nielsen. (2019). Practical Time Series Analysis: Prediction with Statistics and Machine Learning. O'Reilly Media.
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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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