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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
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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Predikcia inflácie vybranými metódami strojového učenia
 Číriová, Nora
The thesis is dealing with the assessment of how effective is the inflation forecast based on choosen indicators of inflation with the help of artificial intelligence in the Slovak Republic and the European Monetary Union. Specifically, it is about two methods of machine learning and those are the method of regression tree and the algorithm of nearest neighbors. Alignment of time series of inflation in the first part of the practical part is carried out by using current values of inflation factors. The second part is intended to equalising time series of inflation lagged values of these factors. Owing to the results we can select factors that have the greatest impact on the inflation.
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