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Lecturer(s)
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Voženílek Vít, prof. RNDr. CSc.
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Ivan Igor, doc. Ing. Ph.D.
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Course content
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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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Learning activities and teaching methods
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unspecified
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Learning outcomes
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The aim of the course is to acquaint students with spatio-temporal data analysis.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
Specialized text and oral expert debate.
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Recommended literature
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DiBiase D., DeMers M., Johnson A., Kemp K., Luck A.T., Plewe B., Wentz E. (Eds.). (2006). Geographic Information Science and Technology Body of Knowledge. UCGIS. Association of American Geographers.
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Gudmundsson J., Laube P., Wolle T. (2017). Movement Patterns in Spatio-Temporal Data. In.
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Hančlová, J., Tvrdý, L. (2003). Úvod do analýzy časových řad. Ekonomická fakulta VŠB.
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Horák J. (2006). Prostorová analýza dat. VŠB.
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Keogh E. Indexing and Mining Time Series Data. In.
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Kollios G., Vlachos M., Gunopulos D. (2017). Trajectories, Discovering Similar. In.
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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.
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Meer Freek D. (1992). Introduction to Geostatistics. ITC Enschede.
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Miller, J. H. (2017). Time Geography. Encyklopedia GIS Springer.
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