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The course presents principles and applications of various time series analysis methods. Traditional linear approaches are shown, as well as techniques suitable for study of nonlinear and chaotic signals.
Last update: Mikšovský Jiří, doc. Mgr., Ph.D. (14.05.2023)
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To present basic principles of linear and nonlinear time series analysis. Last update: JIMI/MFF.CUNI.CZ (30.04.2008)
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Presenting solution to sample time series analysis problems. Last update: Mikšovský Jiří, doc. Mgr., Ph.D. (29.04.2020)
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Pollock D. S. G. (1999): A Handbook of Time-Series Analysis, Signal Processing and Dynamics. Academic Press, 733 pp.
Von Storch H., Zwiers F. W. (1999): Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.
Abarbanel H. D. I. (1996): Analysis of observed chaotic data. Springer, 272 pp.
Kantz H., Schreiber T. (1997): Nonlinear Time Series Analysis. Cambridge University Press, 304 pp. Last update: JIMI/MFF.CUNI.CZ (21.04.2008)
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Lecture, complemented by a practical exercise.
In summer semester 2020/2021: via ZOOM at https://cuni-cz.zoom.us/j/6514300600 Last update: Mikšovský Jiří, doc. Mgr., Ph.D. (26.02.2021)
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Udělení zkoušky je vázáno na prezentaci zápočtových úloh; zkouška má formu diskuse nad zápočtovou prezentací a má ověřit znalost použitých technik a jejich souvislostí, v rozsahu daném sylabem. Last update: Mikšovský Jiří, doc. Mgr., Ph.D. (08.10.2017)
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Basic concepts: Time series, basic characteristics.
Linear methods: AR and ARMA models. Spectral methods, spectral filters, wavelet transform. Dimensionality reduction techniques, principal component analysis, canonical correlation analysis.
Nonlinear methods: Nonlinear and chaotic series. Chaoticity and nonlinearity, behavior of nonlinear systems, attractors and strange attractors. Phase space reconstruction from time series. Time delay method, multivariate approach. Fractal dimension, correlation integral, average mutual information, Lyapunov exponents, entropy and their quantification from time series. Tests for nonlinearity in time series. Time reversibility, surrogate data tests. Method of local models. Neural networks. Multilayer perceptron, backpropagation of error. RBF neural networks. Nonlinear principal component analysis. Last update: JIMI/MFF.CUNI.CZ (30.04.2008)
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