The introductory part of the course introduces students to problems of time series and selected methods of their analysis and prediction - smoothing of time series, decomposition of time series, prediction using regression models, exponential smoothing, ARIMA, SARIMA models. Furthermore, aspects of spatio-temporal data, their types and methods of exploratory analysis of these data are presented. The most important part of the course deals with the evaluation of space-time clustering using methods of visual analytics, clustering methods and other statistical methods. In conclusion, the space is devoted to space-time models, mainly single-parameter models, but also multi-parameter models.
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Gudmundsson J., Laube P., Wolle T. (2017). Movement Patterns in Spatio-Temporal Data. In: Shekhar S., Xiong H., Zhou X. (eds) Encyclopedia of GIS. Springer, Cham.
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Hančlová, J., Tvrdý, L. (2003). Úvod do analýzy časových řad. Ekonomická fakulta VŠB-TU Ostrava.
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Horák J. (2006). Prostorová analýza dat. VŠB-TU Ostrava.
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Keogh E. Indexing and Mining Time Series Data. In: Shekhar S., Xiong H., Zhou X. (eds) Encyclopedia of GIS. Springer, Cham.
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Kollios G., Vlachos M., Gunopulos D. (2017). Trajectories, Discovering Similar. In: Shekhar S., Xiong H., Zhou X. (eds) Encyclopedia of GIS. Springer, Cham.
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Křivý I. (2012). Analýza časových řad. Ostrava.
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Longley, P., Goodchild M.F., Maguire D., Rhind D. (2015). Geographical Information Sciense and Systems. Wiley.
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Miller, J. H. (2017). Time Geography. Encyklopedia GIS Springer.
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