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Predikcia inflácie vybranými metódami strojového učenia v krajinách V4
 Číriová, Nora
The thesis analyzes the accuracy of the multi-step inflation forecast using se-lected methods of machine learning through inflationary factors in the Visegrad group countries. The methods that were applied in the work analysis are the re-gression of tree methods and the algorithm method to the k-nearest neighbors. Based on the regression tree method, we are able to identify factors that are most prominent in price level development. The output of the analysis consists of 8 models, the suitability and accuracy of which are discussed. The results of the em-pirical analysis are compared with the assumptions that were presented before the analysis has begun. This suggests that methods are not suitable for multi-step inflation prediction.
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